Training an artificial intelligence engine for most appropriate actions

ABSTRACT

A system for guiding interactions with a user device requests a response from a plurality of users, stores the response as response data forming a subset of a personal data set of each of the responding users, and generates a predictive model during training of a machine learning program utilizing at least one neural network with a training data set including the personal data set of each of the plurality of users. The predictive model predicts a probability of a first one of the users associated with the user device interacting with a first product and/or service by correlating a personal data set of the first one of the users to the personal data set of at least a second one of the users and sends a communication relating to the first product and/or service to the user device when the predicted probability meets or exceeds a threshold value.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application tracing priority toco-pending U.S. Application serial number 17/661,556 filed on May 1,2022, the entirety of which is herein expressly incorporated byreference.

FIELD OF THE INVENTION

This invention relates to the use of machine learning processes forpredicting the most appropriate product and/or service to offer to auser based on the characteristics and past behaviors of the user.

BACKGROUND OF THE INVENTION

It is known for a business entity to utilize certain giveaways,sweepstakes, drawings, or other contests in order to strengthen therelationship between the business entity and the participants in thecontest, which may correspond to customers and/or prospective customersof the business entity. The offer of a prize, such as a vacation,automobile, or monetary reward, gives a positive impression of thebusiness entity to the participant, and may also may incentivizecontinued interaction between the participant and the business entitythroughout the course of the contest, as applicable. Such contests mayalso be linked to popular culture or current events, such as having alink to a specific product, sporting event, entertainment franchise, orcelebrity, which allows for the participant in the contest to make ashared association between the business entity and the correspondingtopic of interest. Such positive interactions occurring between theparticipant and the business entity can accordingly increase futureengagement therebetween, which can in turn offer additionalopportunities for the business entity to offer products and/or servicesto the participant.

Such contests or similar customer engagements also provide anopportunity for the corresponding business entity to acquire informationregarding each of the participants thereto. For example, it is commonfor each participant to enter certain information regarding the identityand contact information thereof when submitting an entry into such acontest. Such contests may also request the approval for futurecommunications or offers to be sent to the participant.

It is therefore desirable to produce a system and method utilizing thebeneficial aspects of conducting such a contest in order to acquireadditional relevant information regarding each participant in thecontest, where such information can be utilized to improve engagementbetween the business entity and each of the participants. It is alsodesirable to produce a system and method configured to determine anappropriate action to be taken by the business entity in reaction to thereceipt of such information regarding each of the participants.

SUMMARY OF THE INVENTION

Embodiments of the present invention address the above needs and/orachieve other advantages by providing apparatuses and methods thatpredict the future actions or behaviors of individuals based on thepersonal data available with respect to such individuals.

Embodiments of the invention include the use of a system for guidinginteractions with a user device. The system comprises a computer withone or more processor and memory, wherein the computer executescomputer-readable instructions to guide the interactions with the userdevice, and a network connection operatively connecting the user deviceto the computer. Upon execution of the computer-readable instructions,the computer performs steps comprising: requesting a response from aplurality of users; storing the response of each of the responding usersas response data, the response data forming a subset of a personal dataset of each of the responding users; generating a predictive modelduring training of a machine learning program utilizing at least oneneural network, a training data set utilized during the training of themachine learning program comprising the personal data set of each of theplurality of users; predicting, by the predictive model, a probabilityof a first one of the users associated with the user device interactingwith a first product and/or service, the predicting of the probabilityincluding the predictive model correlating a personal data set of thefirst one of the users to the personal data set of at least a second oneof the users; and sending, via the network connection, a communicationto the user device of the first one of the users when the predictedprobability meets or exceeds a threshold value, the communicationincluding content relating to the first product and/or service.

According to embodiments of the invention, a method of interacting witha user device comprising the steps of: requesting a response from aplurality of users; storing the response of each of the responding usersas response data, the response data forming a subset of a personal dataset of each of the responding users; generating a predictive modelduring training of a machine learning program utilizing at least oneneural network, a training data set utilized during the training of themachine learning program comprising the personal data set of each of theplurality of users; predicting, by the predictive model, a probabilityof a first one of the users associated with the user device interactingwith a first product and/or service, the predicting of the probabilityincluding the predictive model correlating a personal data set of thefirst one of the users to the personal data set of at least a second oneof the users; and sending, via the network connection, a communicationto the user device of the first one of the users when the predictedprobability meets or exceeds a threshold value, the communicationincluding content relating to the first product and/or service.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined in yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, wherein:

FIG. 1 illustrates an enterprise system, and environment thereof,according to at least one embodiment.

FIG. 2A is a diagram of a feedforward network, according to at least oneembodiment, utilized in machine learning

FIG. 2B is a diagram of a convolution neural network, according to atleast one embodiment, utilized in machine learning.

FIG. 2C is a diagram of a portion of the convolution neural network ofFIG. 2B, according to at least one embodiment, illustrating assignedweights at connections or neurons.

FIG. 3 is a diagram representing an exemplary weighted sum computationin a node in an artificial neural network.

FIG. 4 is a diagram of a Recurrent Neural Network RNN, according to atleast one embodiment, utilized in machine learning.

FIG. 5 is a schematic logic diagram of an artificial intelligenceprogram including a front-end and a back-end algorithm.

FIG. 6 is a flow chart representing a method, according to at least oneembodiment, of model development and deployment by machine learning.

FIG. 7 is a flow chart representing a method, according to at least oneembodiment, of predicting a probability of a change in user data basedon a personal data profile of the user and reacting to this predictionwith an appropriate action.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to like elements throughout.Unless described or implied as exclusive alternatives, featuresthroughout the drawings and descriptions should be taken as cumulative,such that features expressly associated with some particular embodimentscan be combined with other embodiments. Unless defined otherwise,technical and scientific terms used herein have the same meaning ascommonly understood to one of ordinary skill in the art to which thepresently disclosed subject matter pertains.

The exemplary embodiments are provided so that this disclosure will beboth thorough and complete, and will fully convey the scope of theinvention and enable one of ordinary skill in the art to make, use, andpractice the invention.

The terms “coupled,” “fixed,” “attached to,” “communicatively coupledto,” “operatively coupled to,” and the like refer to both (i) directconnecting, coupling, fixing, attaching, communicatively coupling; and(ii) indirect connecting coupling, fixing, attaching, communicativelycoupling via one or more intermediate components or features, unlessotherwise specified herein. “Communicatively coupled to” and“operatively coupled to” can refer to physically and/or electricallyrelated components.

Embodiments of the present invention described herein, with reference toflowchart illustrations and/or block diagrams of methods or apparatuses(the term “apparatus” includes systems and computer program products),will be understood such that each block of the flowchart illustrationsand/or block diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce aparticular machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create mechanisms for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer readablememory produce an article of manufacture including instructions, whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions, which execute on the computer or other programmableapparatus, provide steps for implementing the functions/acts specifiedin the flowchart and/or block diagram block or blocks. Alternatively,computer program implemented steps or acts may be combined with operatoror human implemented steps or acts in order to carry out an embodimentof the invention.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations, modifications, andcombinations of the herein described embodiments can be configuredwithout departing from the scope and spirit of the invention. Therefore,it is to be understood that, within the scope of the included claims,the invention may be practiced other than as specifically describedherein.

FIG. 1 illustrates a system 100 and environment thereof, according to atleast one embodiment, by which a user 110 benefits through use ofservices and products of an enterprise system 200. The user 110 accessesservices and products by use of one or more user devices, illustrated inseparate examples as a computing device 104 and a mobile device 106,which may be, as non-limiting examples, a smart phone, a portabledigital assistant (PDA), a pager, a mobile television, a gaming device,a laptop computer, a camera, a video recorder, an audio/video player,radio, a GPS device, or any combination of the aforementioned, or otherportable device with processing and communication capabilities. In theillustrated example, the mobile device 106 is illustrated in FIG. 1 ashaving exemplary elements, the below descriptions of which apply as wellto the computing device 104, which can be, as non-limiting examples, adesktop computer, a laptop computer, or other user-accessible computingdevice.

Furthermore, the user device, referring to either or both of thecomputing device 104 and the mobile device 106, may be or include aworkstation, a server, or any other suitable device, including a set ofservers, a cloud-based application or system, or any other suitablesystem, adapted to execute, for example any suitable operating system,including Linux, UNIX, Windows, macOS, iOS, Android and any other knownoperating system used on personal computers, central computing systems,phones, and other devices.

The user 110 can be an individual, a group, or any entity in possessionof or having access to the user device, referring to either or both ofthe mobile device 104 and computing device 106, which may be personal orpublic items. Although the user 110 may be singly represented in somedrawings, at least in some embodiments according to these descriptionsthe user 110 is one of many such that a market or community of users,consumers, customers, business entities, government entities, clubs, andgroups of any size are all within the scope of these descriptions.

The user device, as illustrated with reference to the mobile device 106,includes components such as, at least one of each of a processing device120, and a memory device 122 for processing use, such as random accessmemory (RAM), and read-only memory (ROM). The illustrated mobile device106 further includes a storage device 124 including at least one of anon-transitory storage medium, such as a microdrive, for long-term,intermediate-term, and short-term storage of computer-readableinstructions 126 for execution by the processing device 120. Forexample, the instructions 126 can include instructions for an operatingsystem and various applications or programs 130, of which theapplication 132 is represented as a particular example. The storagedevice 124 can store various other data items 134, which can include, asnon-limiting examples, cached data, user files such as those forpictures, audio and/or video recordings, files downloaded or receivedfrom other devices, and other data items preferred by the user orrequired or related to any or all of the applications or programs 130.

The memory device 122 is operatively coupled to the processing device120. As used herein, memory includes any computer readable medium tostore data, code, or other information. The memory device 122 mayinclude volatile memory, such as volatile Random Access Memory (RAM)including a cache area for the temporary storage of data. The memorydevice 122 may also include non-volatile memory, which can be embeddedand/or may be removable. The non-volatile memory can additionally oralternatively include an electrically erasable programmable read-onlymemory (EEPROM), flash memory or the like.

The memory device 122 and storage device 124 can store any of a numberof applications which comprise computer-executable instructions and codeexecuted by the processing device 120 to implement the functions of themobile device 106 described herein. For example, the memory device 122may include such applications as a conventional web browser application.These applications also typically provide a graphical user interface(GUI) on the display 140 that allows the user 110 to communicate withthe mobile device 106, and, for example a mobile banking system, and/orother devices or systems. In one embodiment, when the user 110 decidesto enroll in a mobile banking program, the user 110 downloads orotherwise obtains the mobile banking system client application from amobile banking system, for example enterprise system 200, or from adistinct application server. In other embodiments, the user 110interacts with a mobile banking system via a web browser applicationcapable of performing the same or similar tasks to the mobile bankingsystem client application. As used hereinafter, each of the softwareapplication associated with the enterprise system 200 and the analogousweb browser application capable of performing the same or similar tasksare denoted by reference numeral 132, which may refer to a mobilebanking system client application capable of operating on either of theuser devices 104, 106.

The processing device 120, and other processors described herein,generally include circuitry for implementing communication and/or logicfunctions of the mobile device 106. For example, the processing device120 may include a digital signal processor, a microprocessor, andvarious analog to digital converters, digital to analog converters,and/or other support circuits. Control and signal processing functionsof the mobile device 106 are allocated between these devices accordingto their respective capabilities. The processing device 120 thus mayalso include the functionality to encode and interleave messages anddata prior to modulation and transmission. The processing device 120 canadditionally include an internal data modem. Further, the processingdevice 120 may include functionality to operate one or more softwareprograms, which may be stored in the memory device 122. For example, theprocessing device 120 may be capable of operating a connectivityprogram, such as the previously described web browser application. Theweb browser application may then allow the mobile device 106 to transmitand receive web content, such as, for example, location-based contentand/or other web page content, according to a Wireless ApplicationProtocol (WAP), Hypertext Transfer Protocol (HTTP), and/or the like. Theapplication 132 related to the enterprise system 200 may be configuredto operate in similar fashion for transmitting such web content.

The memory device 122 and storage device 124 can each also store any ofa number of pieces of information, and data, used by the user device andthe applications and devices that facilitate functions of the userdevice, or are in communication with the user device, to implement thefunctions described herein and others not expressly described. Forexample, the storage device may include such data as user authenticationinformation, etc.

The processing device 120, in various examples, can operatively performcalculations, can process instructions for execution, and can manipulateinformation. The processing device 120 can execute machine-executableinstructions stored in the storage device 124 and/or memory device 122to thereby perform methods and functions as described or implied herein,for example by one or more corresponding flow charts expressly providedor implied as would be understood by one of ordinary skill in the art towhich the subject matters of these descriptions pertain. The processingdevice 120 can be or can include, as non-limiting examples, a centralprocessing unit (CPU), a microprocessor, a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a programmable logic device (PLD), a digital signal processor(DSP), a field programmable gate array (FPGA), a state machine, acontroller, gated or transistor logic, discrete physical hardwarecomponents, and combinations thereof. In some embodiments, particularportions or steps of methods and functions described herein areperformed in whole or in part by way of the processing device 120, whilein other embodiments methods and functions described herein includecloud-based computing in whole or in part such that the processingdevice 120 facilitates local operations including, as non-limitingexamples, communication, data transfer, and user inputs and outputs suchas receiving commands from and providing displays to the user.

The mobile device 106, as illustrated, includes an input and outputsystem 136, referring to, including, or operatively coupled with, userinput devices and user output devices, which are operatively coupled tothe processing device 120. The user output devices include a display 140(e.g., a liquid crystal display or the like), which can be, as anon-limiting example, a touch screen of the mobile device 106, whichserves both as an output device, by providing graphical and text indiciaand presentations for viewing by one or more user 110, and as an inputdevice, by providing virtual buttons, selectable options, a virtualkeyboard, and other indicia that, when touched, control the mobiledevice 106 by user action. The user output devices include a speaker 144or other audio device. The user input devices, which allow the mobiledevice 106 to receive data and actions such as button manipulations andtouches from a user such as the user 110, may include any of a number ofdevices allowing the mobile device 106 to receive data from a user, suchas a keypad, keyboard, touch-screen, touchpad, microphone 142, mouse,joystick, other pointer device, button, soft key, and/or other inputdevice(s). The user interface may also include a camera 146, such as adigital camera.

Further non-limiting examples include, one or more of each, any, and allof a wireless or wired keyboard, a mouse, a touchpad, a button, aswitch, a light, an LED, a buzzer, a bell, a printer and/or other userinput devices and output devices for use by or communication with theuser 110 in accessing, using, and controlling, in whole or in part, theuser device, referring to either or both of the computing device 104 anda mobile device 106. Inputs by one or more user 110 can thus be made viavoice, text or graphical indicia selections. For example, such inputs insome examples correspond to user-side actions and communications seekingservices and products of the enterprise system 200, and at least someoutputs in such examples correspond to data representing enterprise-sideactions and communications in two-way communications between a user 110and an enterprise system 200.

The mobile device 106 may also include a positioning device 108, whichcan be for example a global positioning system device (GPS) configuredto be used by a positioning system to determine a location of the mobiledevice 106. For example, the positioning system device 108 may include aGPS transceiver. In some embodiments, the positioning system device 108includes an antenna, transmitter, and receiver. For example, in oneembodiment, triangulation of cellular signals may be used to identifythe approximate location of the mobile device 106. In other embodiments,the positioning device 108 includes a proximity sensor or transmitter,such as an RFID tag, that can sense or be sensed by devices known to belocated proximate a merchant or other location to determine that theconsumer mobile device 106 is located proximate these known devices.

In the illustrated example, a system intraconnect 138, connects, forexample electrically, the various described, illustrated, and impliedcomponents of the mobile device 106. The intraconnect 138, in variousnon-limiting examples, can include or represent, a system bus, ahigh-speed interface connecting the processing device 120 to the memorydevice 122, individual electrical connections among the components, andelectrical conductive traces on a motherboard common to some or all ofthe above-described components of the user device. As discussed herein,the system intraconnect 138 may operatively couple various componentswith one another, or in other words, electrically connects thosecomponents, either directly or indirectly - by way of intermediatecomponent(s) - with one another.

The user device, referring to either or both of the computing device 104and the mobile device 106, with particular reference to the mobiledevice 106 for illustration purposes, includes a communication interface150, by which the mobile device 106 communicates and conductstransactions with other devices and systems. The communication interface150 may include digital signal processing circuitry and may providetwo-way communications and data exchanges, for example wirelessly viawireless communication device 152, and for an additional or alternativeexample, via wired or docked communication by mechanical electricallyconductive connector 154. Communications may be conducted via variousmodes or protocols, of which GSM voice calls, SMS, EMS, MMS messaging,TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are all non-limiting andnon-exclusive examples. Thus, communications can be conducted, forexample, via the wireless communication device 152, which can be orinclude a radio-frequency transceiver, a Bluetooth device, Wi-Fi device,a Near-field communication device, and other transceivers. In addition,GPS (Global Positioning System) may be included for navigation andlocation-related data exchanges, ingoing and/or outgoing. Communicationsmay also or alternatively be conducted via the connector 154 for wiredconnections such by USB, Ethernet, and other physically connected modesof data transfer.

The processing device 120 is configured to use the communicationinterface 150 as, for example, a network interface to communicate withone or more other devices on a network. In this regard, thecommunication interface 150 utilizes the wireless communication device152 as an antenna operatively coupled to a transmitter and a receiver(together a “transceiver”) included with the communication interface150. The processing device 120 is configured to provide signals to andreceive signals from the transmitter and receiver, respectively. Thesignals may include signaling information in accordance with the airinterface standard of the applicable cellular system of a wirelesstelephone network. In this regard, the mobile device 106 may beconfigured to operate with one or more air interface standards,communication protocols, modulation types, and access types. By way ofillustration, the mobile device 106 may be configured to operate inaccordance with any of a number of first, second, third, fourth,fifth-generation communication protocols and/or the like. For example,the mobile device 106 may be configured to operate in accordance withsecond-generation (2G) wireless communication protocols IS-136 (timedivision multiple access (TDMA)), GSM (global system for mobilecommunication), and/or IS-95 (code division multiple access (CDMA)), orwith third-generation (3G) wireless communication protocols, such asUniversal Mobile Telecommunications System (UMTS), CDMA2000, widebandCDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), withfourth-generation (4G) wireless communication protocols such asLong-Term Evolution (LTE), fifth-generation (5G) wireless communicationprotocols, Bluetooth Low Energy (BLE) communication protocols such asBluetooth 5.0, ultra-wideband (UWB) communication protocols, and/or thelike. The mobile device 106 may also be configured to operate inaccordance with non-cellular communication mechanisms, such as via awireless local area network (WLAN) or other communication/data networks.

The communication interface 150 may also include a payment networkinterface. The payment network interface may include software, such asencryption software, and hardware, such as a modem, for communicatinginformation to and/or from one or more devices on a network. Forexample, the mobile device 106 may be configured so that it can be usedas a credit or debit card by, for example, wirelessly communicatingaccount numbers or other authentication information to a terminal of thenetwork. Such communication could be performed via transmission over awireless communication protocol such as the Near-field communicationprotocol.

The mobile device 106 further includes a power source 128, such as abattery, for powering various circuits and other devices that are usedto operate the mobile device 106. Embodiments of the mobile device 106may also include a clock or other timer configured to determine and, insome cases, communicate actual or relative time to the processing device120 or one or more other devices. For further example, the clock mayfacilitate timestamping transmissions, receptions, and other data forsecurity, authentication, logging, polling, data expiry, and forensicpurposes.

System 100 as illustrated diagrammatically represents at least oneexample of a possible implementation, where alternatives, additions, andmodifications are possible for performing some or all of the describedmethods, operations and functions. Although shown separately, in someembodiments, two or more systems, servers, or illustrated components mayutilized. In some implementations, the functions of one or more systems,servers, or illustrated components may be provided by a single system orserver. In some embodiments, the functions of one illustrated system orserver may be provided by multiple systems, servers, or computingdevices, including those physically located at a central facility, thoselogically local, and those located as remote with respect to each other.

The enterprise system 200 can offer any number or type of services andproducts to one or more users 110. In some examples, an enterprisesystem 200 offers products. In some examples, an enterprise system 200offers services. Use of “service(s)” or “product(s)” thus relates toeither or both in these descriptions. With regard, for example, toonline information and financial services, “service” and “product” aresometimes termed interchangeably. In non-limiting examples, services andproducts include retail services and products, information services andproducts, custom services and products, predefined or pre-offeredservices and products, consulting services and products, advisingservices and products, forecasting services and products, internetproducts and services, social media, and financial services andproducts, which may include, in non-limiting examples, services andproducts relating to banking, checking, savings, investments, creditcards, automatic-teller machines, debit cards, loans, mortgages,personal accounts, business accounts, account management, creditreporting, credit requests, and credit scores.

To provide access to, or information regarding, some or all the servicesand products of the enterprise system 200, automated assistance may beprovided by the enterprise system 200. For example, automated access touser accounts and replies to inquiries may be provided byenterprise-side automated voice, text, and graphical displaycommunications and interactions. In at least some examples, any numberof human agents 210, can be employed, utilized, authorized or referredby the enterprise system 200. Such human agents 210 can be, asnon-limiting examples, point of sale or point of service (POS)representatives, online customer service assistants available to users110, advisors, managers, sales team members, and referral agents readyto route user requests and communications to preferred or particularother agents, human or virtual.

Human agents 210 may utilize agent devices 212 to serve users in theirinteractions to communicate and take action. The agent devices 212 canbe, as non-limiting examples, computing devices, kiosks, terminals,smart devices such as phones, and devices and tools at customer servicecounters and windows at POS locations. In at least one example, thediagrammatic representation of the components of the user device 106 inFIG. 1 applies as well to one or both of the computing device 104 andthe agent devices 212.

Agent devices 212 individually or collectively include input devices andoutput devices, including, as non-limiting examples, a touch screen,which serves both as an output device by providing graphical and textindicia and presentations for viewing by one or more agent 210, and asan input device by providing virtual buttons, selectable options, avirtual keyboard, and other indicia that, when touched or activated,control or prompt the agent device 212 by action of the attendant agent210. Further non-limiting examples include, one or more of each, any,and all of a keyboard, a mouse, a touchpad, a joystick, a button, aswitch, a light, an LED, a microphone serving as input device forexample for voice input by a human agent 210, a speaker serving as anoutput device, a camera serving as an input device, a buzzer, a bell, aprinter and/or other user input devices and output devices for use by orcommunication with a human agent 210 in accessing, using, andcontrolling, in whole or in part, the agent device 212.

Inputs by one or more human agents 210 can thus be made via voice, textor graphical indicia selections. For example, some inputs received by anagent device 212 in some examples correspond to, control, or promptenterprise-side actions and communications offering services andproducts of the enterprise system 200, information thereof, or accessthereto. At least some outputs by an agent device 212 in some examplescorrespond to, or are prompted by, user-side actions and communicationsin two-way communications between a user 110 and an enterprise-sidehuman agent 210.

From a user perspective experience, an interaction in some exampleswithin the scope of these descriptions begins with direct or firstaccess to one or more human agents 210 in person, by phone, or onlinefor example via a chat session or website function or feature. In otherexamples, a user is first assisted by a virtual agent 214 of theenterprise system 200, which may satisfy user requests or prompts byvoice, text, or online functions, and may refer users to one or morehuman agents 210 once preliminary determinations or conditions are madeor met.

A computing system 206 of the enterprise system 200 may includecomponents such as, at least one of each of a processing device 220, anda memory device 222 for processing use, such as random access memory(RAM), and read-only memory (ROM). The illustrated computing system 206further includes a storage device 224 including at least onenon-transitory storage medium, such as a microdrive, for long-term,intermediate-term, and short-term storage of computer-readableinstructions 226 for execution by the processing device 220. Forexample, the instructions 226 can include instructions for an operatingsystem and various applications or programs 230, of which theapplication 232 is represented as a particular example. The storagedevice 224 can store various other data 234, which can include, asnon-limiting examples, cached data, and files such as those for useraccounts, user profiles, account balances, and transaction histories,files downloaded or received from other devices, and other data itemspreferred by the user or required or related to any or all of theapplications or programs 230.

The computing system 206, in the illustrated example, includes aninput/output system 236, referring to, including, or operatively coupledwith input devices and output devices such as, in a non-limitingexample, agent devices 212, which have both input and outputcapabilities.

In the illustrated example, a system intraconnect 238 electricallyconnects the various above-described components of the computing system206. In some cases, the intraconnect 238 operatively couples componentsto one another, which indicates that the components may be directly orindirectly connected, such as by way of one or more intermediatecomponents. The intraconnect 238, in various non-limiting examples, caninclude or represent, a system bus, a high-speed interface connectingthe processing device 220 to the memory device 222, individualelectrical connections among the components, and electrical conductivetraces on a motherboard common to some or all of the above-describedcomponents of the user device.

The computing system 206, in the illustrated example, includes acommunication interface 250, by which the computing system 206communicates and conducts transactions with other devices and systems.The communication interface 250 may include digital signal processingcircuitry and may provide two-way communications and data exchanges, forexample wirelessly via wireless device 252, and for an additional oralternative example, via wired or docked communication by mechanicalelectrically conductive connector 254. Communications may be conductedvia various modes or protocols, of which GSM voice calls, SMS, EMS, MMSmessaging, TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are allnon-limiting and non-exclusive examples. Thus, communications can beconducted, for example, via the wireless device 252, which can be orinclude a radio-frequency transceiver, a Bluetooth device, Wi-Fi device,Near-field communication device, and other transceivers. In addition,GPS (Global Positioning System) may be included for navigation andlocation-related data exchanges, ingoing and/or outgoing. Communicationsmay also or alternatively be conducted via the connector 254 for wiredconnections such as by USB, Ethernet, and other physically connectedmodes of data transfer.

The processing device 220, in various examples, can operatively performcalculations, can process instructions for execution, and can manipulateinformation. The processing device 220 can execute machine-executableinstructions stored in the storage device 224 and/or memory device 222to thereby perform methods and functions as described or implied herein,for example by one or more corresponding flow charts expressly providedor implied as would be understood by one of ordinary skill in the art towhich the subjects matters of these descriptions pertain. The processingdevice 220 can be or can include, as non-limiting examples, a centralprocessing unit (CPU), a microprocessor, a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a programmable logic device (PLD), a digital signal processor(DSP), a field programmable gate array (FPGA), a state machine, acontroller, gated or transistor logic, discrete physical hardwarecomponents, and combinations thereof.

Furthermore, the computing device 206, may be or include a workstation,a server, or any other suitable device, including a set of servers, acloud-based application or system, or any other suitable system, adaptedto execute, for example any suitable operating system, including Linux,UNIX, Windows, macOS, iOS, Android, and any known other operating systemused on personal computer, central computing systems, phones, and otherdevices.

The user devices, referring to either or both of the mobile device 104and computing device 106, the agent devices 212, and the enterprisecomputing system 206, which may be one or any number centrally locatedor distributed, are in communication through one or more networks,referenced as network 258 in FIG. 1 .

Network 258 provides wireless or wired communications among thecomponents of the system 100 and the environment thereof, includingother devices local or remote to those illustrated, such as additionalmobile devices, servers, and other devices communicatively coupled tonetwork 258, including those not illustrated in FIG. 1 . The network 258is singly depicted for illustrative convenience, but may include morethan one network without departing from the scope of these descriptions.In some embodiments, the network 258 may be or provide one or morecloud-based services or operations. The network 258 may be or include anenterprise or secured network, or may be implemented, at least in part,through one or more connections to the Internet. A portion of thenetwork 258 may be a virtual private network (VPN) or an Intranet. Thenetwork 258 can include wired and wireless links, including, asnon-limiting examples, 802.11a/b/g/n/ac, 802.20, WiMax, LTE, and/or anyother wireless link. The network 258 may include any internal orexternal network, networks, sub-network, and combinations of suchoperable to implement communications between various computingcomponents within and beyond the illustrated environment 100. Thenetwork 258 may communicate, for example, Internet Protocol (IP)packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells,voice, video, data, and other suitable information between networkaddresses. The network 258 may also include one or more local areanetworks (LANs), radio access networks (RANs), metropolitan areanetworks (MANs), wide area networks (WANs), all or a portion of theinternet and/or any other communication system or systems at one or morelocations.

Two external systems 202 and 204 are expressly illustrated in FIG. 1 ,representing any number and variety of data sources, users, consumers,customers, business entities, banking systems, government entities,clubs, and groups of any size are all within the scope of thedescriptions. In at least one example, the external systems 202 and 204represent automatic teller machines (ATMs) utilized by the enterprisesystem 200 in serving users 110. In another example, the externalsystems 202 and 204 represent payment clearinghouse or payment railsystems for processing payment transactions, and in another example, theexternal systems 202 and 204 represent third party systems such asmerchant systems configured to interact with the user device 106 duringtransactions and also configured to interact with the enterprise system200 in back-end transactions clearing processes.

In certain embodiments, one or more of the systems such as the userdevice 106, the enterprise system 200, and/or the external systems 202and 204 are, include, or utilize virtual resources. In some cases, suchvirtual resources are considered cloud resources or virtual machines.Such virtual resources may be available for shared use among multipledistinct resource consumers and in certain implementations, virtualresources do not necessarily correspond to one or more specific piecesof hardware, but rather to a collection of pieces of hardwareoperatively coupled within a cloud computing configuration so that theresources may be shared as needed.

As used herein, an artificial intelligence system, artificialintelligence algorithm, artificial intelligence module, program, and thelike, generally refer to computer implemented programs that are suitableto simulate intelligent behavior (i.e., intelligent human behavior)and/or computer systems and associated programs suitable to performtasks that typically require a human to perform, such as tasks requiringvisual perception, speech recognition, decision-making, translation, andthe like. An artificial intelligence system may include, for example, atleast one of a series of associated if-then logic statements, astatistical model suitable to map raw sensory data into symboliccategories and the like, or a machine learning program. A machinelearning program, machine learning algorithm, or machine learningmodule, as used herein, is generally a type of artificial intelligenceincluding one or more algorithms that can learn and/or adjust parametersbased on input data provided to the algorithm. In some instances,machine learning programs, algorithms, and modules are used at least inpart in implementing artificial intelligence (AI) functions, systems,and methods.

Artificial Intelligence and/or machine learning programs may beassociated with or conducted by one or more processors, memory devices,and/or storage devices of a computing system or device. The presentinvention may include a machine learning program that may be executed bythe processor 220 of the computing system 206 associated with theenterprise system 200, and may utilize the data 234 stored to thestorage device 224. It should be appreciated that the AI algorithm orprogram may be incorporated within the existing system architecture orbe configured as a standalone modular component, controller, or the likecommunicatively coupled to the system. An AI program and/or machinelearning program may generally be configured to perform methods andfunctions as described or implied herein, for example by one or morecorresponding flow charts expressly provided or implied as would beunderstood by one of ordinary skill in the art to which the subjectsmatters of these descriptions pertain.

A machine learning program may be configured to implement storedprocessing, such as decision tree learning, association rule learning,artificial neural networks, recurrent artificial neural networks, longshort term memory networks, inductive logic programming, support vectormachines, clustering, Bayesian networks, reinforcement learning,representation learning, similarity and metric learning, sparsedictionary learning, genetic algorithms, k-nearest neighbor (KNN), andthe like. In some embodiments, the machine learning algorithm mayinclude one or more image recognition algorithms suitable to determineone or more categories to which an input, such as data communicated froma visual sensor or a file in JPEG, PNG or other format, representing animage or portion thereof, belongs. Additionally or alternatively, themachine learning algorithm may include one or more regression algorithmsconfigured to output a numerical value given an input. Further, themachine learning may include one or more pattern recognition algorithms,e.g., a module, subroutine or the like capable of translating text orstring characters and/or a speech recognition module or subroutine. Invarious embodiments, the machine learning module may include a machinelearning acceleration logic, e.g., a fixed function matrixmultiplication logic, in order to implement the stored processes and/oroptimize the machine learning logic training and interface.

One type of algorithm suitable for use in machine learning modules asdescribed herein is an artificial neural network or neural network,taking inspiration from biological neural networks. An artificial neuralnetwork can, in a sense, learn to perform tasks by processing examples,without being programmed with any task-specific rules. A neural networkgenerally includes connected units, neurons, or nodes (e.g., connectedby synapses) and may allow for the machine learning program to improveperformance. A neural network may define a network of functions, whichhave a graphical relationship. As an example, a feedforward network maybe utilized, e.g., an acyclic graph with nodes arranged in layers.

A feedforward network (see, e.g., feedforward network 260 referenced inFIG. 2A) may include a topography with a hidden layer 264 between aninput layer 262 and an output layer 266. The input layer 262, havingnodes commonly referenced in FIG. 2A as input nodes 204 for convenience,communicates input data, variables, matrices, or the like to the hiddenlayer 264, having nodes 274. The hidden layer 264 generates arepresentation and/or transformation of the input data into a form thatis suitable for generating output data. Adjacent layers of thetopography are connected at the edges of the nodes of the respectivelayers, but nodes within a layer typically are not separated by an edge.In at least one embodiment of such a feedforward network, data iscommunicated to the nodes 204 of the input layer, which thencommunicates the data to the hidden layer 264. The hidden layer 264 maybe configured to determine the state of the nodes in the respectivelayers and assign weight coefficients or parameters of the nodes basedon the edges separating each of the layers, e.g., an activation functionimplemented between the input data communicated from the input layer 262and the output data communicated to the nodes 276 of the output layer266. It should be appreciated that the form of the output from theneural network may generally depend on the type of model represented bythe algorithm. Although the feedforward network 260 of FIG. 2A expresslyincludes a single hidden layer 264, other embodiments of feedforwardnetworks within the scope of the descriptions can include any number ofhidden layers. The hidden layers are intermediate the input and outputlayers and are generally where all or most of the computation is done.

Neural networks may perform a supervised learning process where knowninputs and known outputs are utilized to categorize, classify, orpredict a quality of a future input. However, additional or alternativeembodiments of the machine learning program may be trained utilizingunsupervised or semi-supervised training, where none of the outputs orsome of the outputs are unknown, respectively. Typically, a machinelearning algorithm is trained (e.g., utilizing a training data set)prior to modeling the problem with which the algorithm is associated.Supervised training of the neural network may include choosing a networktopology suitable for the problem being modeled by the network andproviding a set of training data representative of the problem.Generally, the machine learning algorithm may adjust the weightcoefficients until any error in the output data generated by thealgorithm is less than a predetermined, acceptable level. For instance,the training process may include comparing the generated output producedby the network in response to the training data with a desired orcorrect output. An associated error amount may then be determined forthe generated output data, such as for each output data point generatedin the output layer. The associated error amount may be communicatedback through the system as an error signal, where the weightcoefficients assigned in the hidden layer are adjusted based on theerror signal. For instance, the associated error amount (e.g., a valuebetween -1 and 1) may be used to modify the previous coefficient, e.g.,a propagated value. The machine learning algorithm may be consideredsufficiently trained when the associated error amount for the outputdata is less than the predetermined, acceptable level (e.g., each datapoint within the output layer includes an error amount less than thepredetermined, acceptable level). Thus, the parameters determined fromthe training process can be utilized with new input data to categorize,classify, and/or predict other values based on the new input data.

An additional or alternative type of neural network suitable for use inthe machine learning program and/or module is a Convolutional NeuralNetwork (CNN). A CNN is a type of feedforward neural network that may beutilized to model data associated with input data having a grid-liketopology. In some embodiments, at least one layer of a CNN may include asparsely connected layer, in which each output of a first hidden layerdoes not interact with each input of the next hidden layer. For example,the output of the convolution in the first hidden layer may be an inputof the next hidden layer, rather than a respective state of each node ofthe first layer. CNNs are typically trained for pattern recognition,such as speech processing, language processing, and visual processing.As such, CNNs may be particularly useful for implementing optical andpattern recognition programs required from the machine learning program.A CNN includes an input layer, a hidden layer, and an output layer,typical of feedforward networks, but the nodes of a CNN input layer aregenerally organized into a set of categories via feature detectors andbased on the receptive fields of the sensor, retina, input layer, etc.Each filter may then output data from its respective nodes tocorresponding nodes of a subsequent layer of the network. A CNN may beconfigured to apply the convolution mathematical operation to therespective nodes of each filter and communicate the same to thecorresponding node of the next subsequent layer. As an example, theinput to the convolution layer may be a multidimensional array of data.The convolution layer, or hidden layer, may be a multidimensional arrayof parameters determined while training the model.

An exemplary convolutional neural network CNN is depicted and referencedas 280 in FIG. 2B. As in the basic feedforward network 260 of FIG. 2A,the illustrated example of FIG. 2B has an input layer 282 and an outputlayer 286. However where a single hidden layer 264 is represented inFIG. 2A, multiple consecutive hidden layers 284A, 284B, and 284C arerepresented in FIG. 2B. The edge neurons represented by white-filledarrows highlight that hidden layer nodes can be connected locally, suchthat not all nodes of succeeding layers are connected by neurons. FIG.2C, representing a portion of the convolutional neural network 280 ofFIG. 2B, specifically portions of the input layer 282 and the firsthidden layer 284A, illustrates that connections can be weighted. In theillustrated example, labels W1 and W2 refer to respective assignedweights for the referenced connections. Two hidden nodes 283 and 285share the same set of weights W1 and W2 when connecting to two localpatches.

Weight defines the impact a node in any given layer has on computationsby a connected node in the next layer. FIG. 3 represents a particularnode 300 in a hidden layer. The node 300 is connected to several nodesin the previous layer representing inputs to the node 300. The inputnodes 301, 302, 303 and 304 are each assigned a respective weight W01,W02, W03, and W04 in the computation at the node 300, which in thisexample is a weighted sum.

An additional or alternative type of feedforward neural network suitablefor use in the machine learning program and/or module is a RecurrentNeural Network (RNN). An RNN may allow for analysis of sequences ofinputs rather than only considering the current input data set. RNNstypically include feedback loops/connections between layers of thetopography, thus allowing parameter data to be communicated betweendifferent parts of the neural network. RNNs typically have anarchitecture including cycles, where past values of a parameterinfluence the current calculation of the parameter, e.g., at least aportion of the output data from the RNN may be used as feedback/input incalculating subsequent output data. In some embodiments, the machinelearning module may include an RNN configured for language processing,e.g., an RNN configured to perform statistical language modeling topredict the next word in a string based on the previous words. TheRNN(s) of the machine learning program may include a feedback systemsuitable to provide the connection(s) between subsequent and previouslayers of the network.

An example for a Recurrent Neural Network RNN is referenced as 400 inFIG. 4 . As in the basic feedforward network 260 of FIG. 2A, theillustrated example of FIG. 4 has an input layer 410 (with nodes 412)and an output layer 440 (with nodes 442). However, where a single hiddenlayer 264 is represented in FIG. 2A, multiple consecutive hidden layers420 and 430 are represented in FIG. 4 (with nodes 422 and nodes 432,respectively). As shown, the RNN 400 includes a feedback connector 404configured to communicate parameter data from at least one node 432 fromthe second hidden layer 430 to at least one node 422 of the first hiddenlayer 420. It should be appreciated that two or more and up to all ofthe nodes of a subsequent layer may provide or communicate a parameteror other data to a previous layer of the RNN network 400. Moreover andin some embodiments, the RNN 400 may include multiple feedbackconnectors 404 (e.g., connectors 404 suitable to communicatively couplepairs of nodes and/or connector systems 404 configured to providecommunication between three or more nodes). Additionally oralternatively, the feedback connector 404 may communicatively couple twoor more nodes having at least one hidden layer between them, i.e., nodesof nonsequential layers of the RNN 400.

In an additional or alternative embodiment, the machine learning programmay include one or more support vector machines. A support vectormachine may be configured to determine a category to which input databelongs. For example, the machine learning program may be configured todefine a margin using a combination of two or more of the inputvariables and/or data points as support vectors to maximize thedetermined margin. Such a margin may generally correspond to a distancebetween the closest vectors that are classified differently. The machinelearning program may be configured to utilize a plurality of supportvector machines to perform a single classification. For example, themachine learning program may determine the category to which input databelongs using a first support vector determined from first and seconddata points/variables, and the machine learning program mayindependently categorize the input data using a second support vectordetermined from third and fourth data points/variables. The supportvector machine(s) may be trained similarly to the training of neuralnetworks, e.g., by providing a known input vector (including values forthe input variables) and a known output classification. The supportvector machine is trained by selecting the support vectors and/or aportion of the input vectors that maximize the determined margin.

As depicted, and in some embodiments, the machine learning program mayinclude a neural network topography having more than one hidden layer.In such embodiments, one or more of the hidden layers may have adifferent number of nodes and/or the connections defined between layers.In some embodiments, each hidden layer may be configured to perform adifferent function. As an example, a first layer of the neural networkmay be configured to reduce a dimensionality of the input data, and asecond layer of the neural network may be configured to performstatistical programs on the data communicated from the first layer. Invarious embodiments, each node of the previous layer of the network maybe connected to an associated node of the subsequent layer (denselayers). Generally, the neural network(s) of the machine learningprogram may include a relatively large number of layers, e.g., three ormore layers, and are referred to as deep neural networks. For example,the node of each hidden layer of a neural network may be associated withan activation function utilized by the machine learning program togenerate an output received by a corresponding node in the subsequentlayer. The last hidden layer of the neural network communicates a dataset (e.g., the result of data processed within the respective layer) tothe output layer. Deep neural networks may require more computationaltime and power to train, but the additional hidden layers providemultistep pattern recognition capability and/or reduced output errorrelative to simple or shallow machine learning architectures (e.g.,including only one or two hidden layers).

Referring now to FIG. 5 and some embodiments, an AI program 502 mayinclude a front-end algorithm 504 and a back-end algorithm 506. Theartificial intelligence program 502 may be implemented on an AIprocessor 520, such as the processing device 120, the processing device220, and/or a dedicated processing device. The instructions associatedwith the front-end algorithm 504 and the back-end algorithm 506 may bestored in an associated memory device and/or storage device of thesystem (e.g., memory device 124 and/or memory device 224)communicatively coupled to the AI processor 520, as shown. Additionallyor alternatively, the system may include one or more memory devicesand/or storage devices (represented by memory 524 in FIG. 5 ) forprocessing use and/or including one or more instructions necessary foroperation of the AI program 502. In some embodiments, the AI program 502may include a deep neural network (e.g., a front-end network 504configured to perform pre-processing, such as feature recognition, and aback-end network 506 configured to perform an operation on the data setcommunicated directly or indirectly to the back-end network 506). Forinstance, the front-end program 506 can include at least one CNN 508communicatively coupled to send output data to the back-end network 506.

Additionally or alternatively, the front-end program 504 can include oneor more AI algorithms 510, 512 (e.g., statistical models or machinelearning programs such as decision tree learning, associate rulelearning, recurrent artificial neural networks, support vector machines,and the like). In various embodiments, the front-end program 504 may beconfigured to include built in training and inference logic or suitablesoftware to train the neural network prior to use (e.g., machinelearning logic including, but not limited to, image recognition, mappingand localization, autonomous navigation, speech synthesis, documentimaging, or language translation). For example, a CNN 508 and/or AIalgorithm 510 may be used for image recognition, input categorization,and/or support vector training. In some embodiments and within thefront-end program 504, an output from an AI algorithm 510 may becommunicated to a CNN 508 or 509, which processes the data beforecommunicating an output from the CNN 508, 509 and/or the front-endprogram 504 to the back-end program 506. In various embodiments, theback-end network 506 may be configured to implement input and/or modelclassification, speech recognition, translation, and the like. Forinstance, the back-end network 506 may include one or more CNNs (e.g,,CNN 514) or dense networks (e.g., dense networks 516), as describedherein.

For instance and in some embodiments of the AI program 502, the programmay be configured to perform unsupervised learning, in which the machinelearning program performs the training process using unlabeled data,e.g., without known output data with which to compare. During suchunsupervised learning, the neural network may be configured to generategroupings of the input data and/or determine how individual input datapoints are related to the complete input data set (e.g., via thefront-end program 504). For example, unsupervised training may be usedto configure a neural network to generate a self-organizing map, reducethe dimensionally of the input data set, and/or to performoutlier/anomaly determinations to identify data points in the data setthat falls outside the normal pattern of the data. In some embodiments,the AI program 502 may be trained using a semi-supervised learningprocess in which some but not all of the output data is known, e.g., amix of labeled and unlabeled data, which may also have the samedistribution.

In some embodiments, the AI program 502 may be accelerated via a machinelearning framework 520 (e.g., hardware). The machine learning frameworkmay include an index of basic operations, subroutines, and the like(primitives) typically implemented by AI and/or machine learningalgorithms. Thus, the AI program 502 may be configured to utilize theprimitives of the framework 520 to perform some or all of thecalculations required by the AI program 502. Primitives suitable forinclusion in the machine learning framework 520 include operationsassociated with training a convolutional neural network (e.g., pools),tensor convolutions, activation functions, basic algebraic subroutinesand programs (e.g., matrix operations, vector operations), numericalmethod subroutines and programs, and the like.

It should be appreciated that the machine learning program may includevariations, adaptations, and alternatives suitable to perform theoperations necessary for the system, and the present disclosure isequally applicable to such suitably configured machine learning and/orartificial intelligence programs, modules, etc. For instance, themachine learning program may include one or more long short-term memory(LSTM) RNNs, convolutional deep belief networks, deep belief networksDBNs, and the like. DBNs, for instance, may be utilized to pre-train theweighted characteristics and/or parameters using an unsupervisedlearning process. Further, the machine learning module may include oneor more other machine learning tools (e.g., Logistic Regression (LR),Naive-Bayes, Random Forest (RF), matrix factorization, and supportvector machines) in addition to, or as an alternative to, one or moreneural networks, as described herein.

FIG. 6 is a flow chart representing a method 600, according to at leastone embodiment, of model development and deployment by machine learning.The method 600 represents at least one example of a machine learningworkflow in which steps are implemented in a machine learning project.

In step 602, a user authorizes, requests, manages, or initiates themachine-learning workflow. This may represent a user such as humanagent, or customer, requesting machine-learning assistance or AIfunctionality to simulate intelligent behavior (such as a virtual agent)or other machine-assisted or computerized tasks that may, for example,entail visual perception, speech recognition, decision-making,translation, forecasting, predictive modelling, and/or suggestions asnon-limiting examples. In a first iteration from the user perspective,step 602 can represent a starting point. However, with regard tocontinuing or improving an ongoing machine learning workflow, step 602can represent an opportunity for further user input or oversight via afeedback loop.

In step 604, data is received, collected, accessed, or otherwiseacquired and entered as can be termed data ingestion. In step 606 thedata ingested in step 604 is preprocessed, for example, by cleaning,and/or transformation such as into a format that the followingcomponents can digest. The incoming data may be versioned to connect adata snapshot with the particularly resulting trained model. As newlytrained models are tied to a set of versioned data, preprocessing stepsare tied to the developed model. If new data is subsequently collectedand entered, a new model will be generated. If the preprocessing step606 is updated with newly ingested data, an updated model will begenerated. Step 606 can include data validation, which focuses onconfirming that the statistics of the ingested data are as expected,such as that data values are within expected numerical ranges, that datasets are within any expected or required categories, and that datacomply with any needed distributions such as within those categories.Step 606 can proceed to step 608 to automatically alert the initiatinguser, other human or virtual agents, and/or other systems, if anyanomalies are detected in the data, thereby pausing or terminating theprocess flow until corrective action is taken.

In step 610, training test data such as a target variable value isinserted into an iterative training and testing loop. In step 612, modeltraining, a core step of the machine learning work flow, is implemented.A model architecture is trained in the iterative training and testingloop. For example, features in the training test data are used to trainthe model based on weights and iterative calculations in which thetarget variable may be incorrectly predicted in an early iteration asdetermined by comparison in step 614, where the model is tested.Subsequent iterations of the model training, in step 612, may beconducted with updated weights in the calculations.

When compliance and/or success in the model testing in step 614 isachieved, process flow proceeds to step 616, where model deployment istriggered. The model may be utilized in AI functions and programming,for example to simulate intelligent behavior, to performmachine-assisted or computerized tasks, of which visual perception,speech recognition, decision-making, translation, forecasting,predictive modelling, and/or automated suggestion generation serve asnon-limiting examples.

The present invention relates to the creation of a predictive model forpredicting the traits and/or behavior of a user 110 based on thetraining of a machine learning program. The machine learning program ofthe present invention is described hereinafter as utilizing the datasets associated with a plurality of the users 110 of the enterprisesystem 200. As mentioned hereinabove, each of the users 110 may be aperson or entity acting as a customer or client of the enterprise system200 that utilizes products and/or services originating at least in partfrom the enterprise system 200 as defined herein, or may otherwise be aperson or entity having an established relationship with the enterprisesystem 200 such that the enterprise system 200 has access to thenecessary personal data regarding each of the participating users 110for making the determinations described hereinafter. The relationshippresent between the enterprise system 200 and each of the users 110 mayinclude one or more of the users 110 having an account with theenterprise system 200 wherein certain interactions between theenterprise system 200 and each of the users 110 may be monitored andrecorded by the computing system 206, as described in greater detailherein. Alternatively, if not already customers or clients, one or moreof the users 110 may be representative of persons or entities that areconsidered to be potential or prospective customers or clients, such asthose persons or entities for which the computing system 206 has thenecessary data allowing for the enterprise system 200 to identify andthen communicate with the corresponding person or entity to offer suchproducts and/or services.

The machine learning program utilizes personal data regarding each ofthe users 110 of the enterprise system 200. The personal data of each ofthe users 110 of the enterprise system 200 may be in the form of thedata 234 stored to the storage device 224 of the computing system 206 asutilized for carrying out the functions of the machine learning programas described herein. The data 234 may originate from various differentsources including the responses of the user 110 to queries from theenterprise system 200, the recorded interactions of the user 110 withthe enterprise system 200, or one or more third-party and externalsources or systems, which may be representative of the previouslydisclosed external systems 202, 204.

The present invention relies upon the enterprise system 200 havingaccess to the personal data associated with each associated user 110 inorder to train the machine learning program and subsequently utilize thepredictive model generated thereby. In some embodiments, the inventionmay be carried out with respect to a user 110 having an establishedaccount with the enterprise system 200, wherein the establishment of anaccount may include the user 110 providing at least some of the entriesof the associated personal data to the enterprise system 200. Theenterprise system 200 may collect data regarding the user 110 bydirectly querying and recording the responses of the user 110. Such datamay be entered via use of the web browser application or softwareapplication 132 associated with the enterprise system 200, and suchinformation may be entered by the user 110 via use of the user device104, 106 executing the corresponding application 132. The data providedto the enterprise system 200 regarding the user 110 may include, asnon-limiting examples, the gender, age, income level, employment status,home ownership status, marital status, citizenship status, etc. of thecorresponding user 110. Any available demographic data regarding theuser 110 may form a portion of the personal data utilized by the machinelearning program with respect the user 110.

If the enterprise system 200 is representative of a financialinstitution or mobile banking system, the personal data accessible tothe enterprise system 200 regarding the user 100 may include dataregarding products and/or services offered to the user 110 by theenterprise system 200 relating to banking, checking, savings,investments, credit cards, automatic-teller machines, debit cards,loans, mortgages, personal accounts, business accounts, accountmanagement, credit reporting, credit requests, and credit scores, asnon-limiting examples. The data may further include files such as thosefor user accounts, user profiles, user account balances, usertransaction histories, user investment portfolios, past communicationswith the user, or files downloaded or received from other devices suchas the user devices 104, 106 of the user 110.

In some circumstances, such as when the enterprise system 200 isrepresentative of a financial institution or mobile banking systemoffering typical banking services and products, the enterprise system200 may have access to data regarding the transactions of the user 110as facilitated by the enterprise system 200. For example, transactionhistories regarding purchases carried out via a credit card or debitcard associated with the enterprise system 200 may be accessible to thecomputing system 206, as well as current or prior account balances ofsuch accounts.

The enterprise system 200 may also be configured to monitor and recordspecific interactions of the user 110 with the enterprise system 200 inattaining additional data regarding the user 110 that may be utilized bythe machine learning program disclosed herein. For example, in the eventthat the user 110 has an account with the enterprise system 200, theuser 110 may be required to provide authentication data to the webbrowser application or software application 132 associated with theenterprise system 200. Following such a login process, the enterprisesystem 200 may monitor and record the interactions of the identifieduser 110 with the interface of the corresponding application 132 inorder to accumulate data associated with the user 110. For example, theenterprise system 200 may monitor data such as the number of logins tothe account of the user 110 in a specified period of time, the frequencyof the logins of the user 110, the duration of time the user 110 remainslogged into the application 132 (while remaining active), and the typesof products and/or services interacted with and/or purchased by the user110 via navigation of the corresponding application 132. Data may alsobe recorded regarding the navigation of the application 132, such asrecording which resources the user 110 has accessed, how long suchresources were accessed, or the like, such as referencing which webaddresses associated with the application 132 have been accessed by theuser 110 or which files related to the application 132 have beenaccessed by the user 110.

The personal data regarding the user 110 may also include data relatingto the account settings of the user 110 as established with respect tothe computing system 206. Such account setting data may be stored to thestorage device 224 of the computing system 206 and may be associatedwith determining how the computing system 206 interacts with the user110 via the corresponding user device 104, 106. For example, suchaccount setting data may include data relating to the frequency ofcommunications sent from the computing system 206 to the user 110 foraccess via the user device 104, 106, under what conditions tocommunicate with the user 110, the content of such communications, thetypes or forms of such communications, the manner in which the interfaceof the web browser application or software application 132 displaysinformation to the user 110, or the information or resources accessibleto the user 110 via navigation of the web browser application orsoftware application 132, as non-limiting examples.

In other circumstances, the personal data may be representative of dataacquired regarding the user 110 during web related activities, such astracking a web browsing history of the user 110, as may be provided by“cookies” or similar tools, or tracking certain communications of theuser 110, such as monitoring certain aspects of the email activity ofthe user 110. If web related activities are monitored, such data maycorrespond to the activities of the user 110 with respect to the webpageor software application 132 associated with the enterprise system 200 ormay relate to the activities of the user 110 with respect to third partyapplications or websites. Such data may be communicated from acorresponding user device 104, 106 used to perform the web browsing tothe computing system 206 for storage to the storage device 224 as a formof the data 234.

The enterprise system 200 may also utilize data originating from one ofthe external systems 202, 204, which may be representative of personaldata accumulated with respect to the user 110 external to the enterprisesystem 200 that is available to or otherwise accessible by the computingsystem 206 via interaction with one or more of the external systems 202,204. The external systems 202, 204 may accordingly be representative ofthird-party data providers configured to communicate data to thecomputing system 206 regarding the user 110. Such data may include acredit history of the user 110 or transactions of the user 110 withrespect to other business entities, as may originate from sources othersthan the enterprise system 200. Further examples include dataoriginating from third party social networks or the like, such ascheck-ins at certain establishments, social connections to other users,posting or commenting histories, or interactions with certain otherusers or business entities. Data regarding a transaction history of theuser 110, whether derived from the relationship between the user 110 andthe enterprise system 200 or the user 110 and a third party externalsystem 202, 204, may include data regarding the establishments at whichthe user 110 has made the purchases, the amounts of such purchases, andpotentially additional information regarding the products and/orservices related to such purchases. Such data may be available viarecords of the credit or debit purchases made by the user with respectto certain establishments as monitored by the third party externalsystem 202, 204.

The personal data collected with respect to each user 110 may becategorized as demographic data regarding the user 110, behavioral dataregarding the activities of the user 110, and behavioral data regardingthe activities of the enterprise system 200 with respect to the user 110(such as data relating to communications from the enterprise system 200to the user 110 regarding educational materials or data relating tooffers for the purchase of products and/or services). The demographicdata generally refers to the data regarding the user 110 thatcorresponds to a trait or characteristic of the user 110 by which theuser 110 may be categorized or classified, whereas the behavioral datagenerally refers to data regarding the recordation of informationregarding the actions of the user 110, the actions of the enterprisesystem 200, or past interactions or transactions occurring between theenterprise system 200 and the user 110.

The personal data set associated with at least some of the users 110 mayalso include at least one entry of response data, wherein such responsedata may refer to data regarding the response(s) of the participatingusers 110 of the enterprise system 200 to one or more queries. Each ofthe users 110 may be alternatively referred to as a respondent of one ormore of the queries when discussing the querying process hereinafter.The response data may be available for only some of the plurality of theusers 110 of the enterprise system 200, depending on the responsivenessof such users 110 to such queries or the distribution of such queriesbeing posed to the plurality of the users 110. As used herein, a querymay refer to any question answered by a respondent for the purpose ofcollecting data regarding the opinions, feelings, thoughts, beliefs,impressions, predictions, and/or observations of the respondent. Theresponse data may be accumulated using any known method so long as theresponse data is recorded in a form configured for use with thecomputing system 206 and the corresponding machine learning programexecuted thereon. In some embodiments, the querying of each of the users110 may be conducted online via the web browser or software application132 corresponding to the enterprise system 200 as operating on the userdevice 104, 106 of the respondent, as explained in greater detailhereinafter.

Each of the queries may be linked to a corresponding contest, giveaway,drawing, or sweepstakes as offered by the enterprise system 200 or anaffiliate thereof, hence the users 110 having provided such responsesmay be indicative of those users 110 having participated in suchcontests, giveaways, drawings, or sweepstakes (collectively referred toas “contests” hereinafter for brevity). In some circumstances, thecorresponding contest has some connection or relationship to a topicsuch as a popular culture event, corporation, persona, celebrity,product, entertainment franchise, sporting event/tournament/league, orthe like. Such a relationship may be utilized in marketing the contestor may include the prize of the contest having a specific relationshipto the topic in question. For example, if the topic associated with thecontest is a sporting event, the sporting event may be utilized inmarketing the contest or may be related to the prize offered as a partof the contest, such as winning tickets to the sporting event inquestion. The manner in which the marketing and/or the prize of thecontest is related to a specific topic may aid in providing increasedengagement of each participating user 110 by relating to a topic ofinterest to the user 110, or to a topic of perceived knowledge of theuser 110 (such as appealing to the ability of the user 110 to predictthe winners of sporting events). Such increased engagement may lead toan increased likelihood of participation in the corresponding contest,and hence access to the query or queries associated with such a contest.

The queries may include content that directly or indirectly relates tothe topic of the marketing materials and/or prize of the contest. Forexample, if an object such as an automobile is being offered as a prize,the queries related to the corresponding contest may relate to whetheror not the participating user 110 has insurance, is satisfied with saidinsurance, or has an interest in changing said insurance. As anotherexample relating to the automobile as the prize, the query may relate towhat products and/or services the user 110 would purchase if notresponsible for a monthly automobile payment. If a monetary prize isoffered, the query may relate to what investment activities or purchasesthe user 110 expects to make following receipt of the monetary prize.The query may include the user 110 providing a preference for orinterest in one or more products and/or services offered by theenterprise system 200 that would be likely to be purchased with such amonetary prize.

The queries may alternatively relate to collecting or updating certaindemographic or behavioral data regarding the user 110 that has otherwisebeen unable to be collected/updated. Such responses may accordingly aidin providing further correlations to similar users 110 by expanding thedata set upon which correlations can be discovered and implemented.

The queries may directly relate to the preferences of the user 110regarding certain account settings or other interactions with thecomputing system 206 and/or enterprise system 200. For example, a querymay request the impressions of the user 110 on the performance of theenterprise system 200 in meeting the needs of the user 110 with respectto certain products and/or services offered by the enterprise system200. As another example, a query may relate to the preferences of theuser 110 regarding the number, form, and content of certaincommunications sent to the user 110 by the computing system 206 as maybe represented in the account settings of the user 110, such as apreference for paperless communication.

The queries may also relate to evaluating certain predictions made bythe machine learning program of the present invention. For example, thepredictive model of the machine learning program may be configured todetermine a propensity for a user 110 to have a certain preference, andthe predictive model may be evaluated, and potentially further trainedand refined, by querying the user 110 regarding the prediction of thispreference.

The queries are not necessarily limited to being associated with aspecific contest or topic of interest. In some circumstances, thequeries may be associated with a survey proactively offered to at leastsome of the users 110 for attaining additional personal data regardingany of the disclosed forms of personal data herein.

Although the response data has been described as originating from theinteractions between the corresponding user 110 and the computing system206 of the enterprise system 200, the response data may originate fromany source without necessarily departing from the scope of the presentinvention. In some embodiments, the querying is conducted by theenterprise system 200 according to any of the processes discussed above,and the resulting response data is stored to the storage device 224 asone form of the data 234 associated therewith. In other embodiments, theresponse data is accumulated by a third party associated with thecontest and/or corresponding survey (or the like), and the resultingresponse data is communicated to the storage device 224 for storage as aform of the data 234. The third party conducting the querying andaccumulating the response data may be representative of one of theexternal systems 202, 204 shown and described as being in communicationwith the computing system 206 with reference to FIG. 1 . In such acircumstance, the user device 104, 106 of the user 110 may be utilizedto respond to the queries via the web browser application or via asoftware application associated with the third party external system202, 204 responsible for making the queries, and such data may becommunicated from the external system 202, 204 to the computing system206 by any known method, or may alternatively be communicated directlyfrom the user device 104, 106 to the computing system 206, as desired.

A response data set associated with each individual user 110, which is asubset of the personal data set of that same user 110, may include adata entry with respect to each query asked of and answered by thecorresponding user 110. In some circumstances, only a single query orsingle set of queries asked substantially contemporaneously may form theresponse data set of the corresponding user 110, whereas in othercircumstances, the corresponding user 110 may include response dataentries with respect to a plurality of independently conductedcontests/surveys, each of which may be associated with one or moreresponse data entries, depending on the format of the contest/survey andthe types of queries posed.

A personal data set associated with any individual user 110 mayaccordingly include entries of any of the different types of datadisclosed hereinabove, including entries relating to demographic data,behavioral data, and response data. Each entry of the personal data setmay be representative of one of the demographic traits of the user 110,one of the behavioral traits of the user 110, one of the behavioraltraits of the computing system 206, or one of the responses of the user110 to a corresponding query. The number or types of entries availablein each personal data set may vary among users 110 depending on therelationship to the enterprise system 200 and the availability of suchdata, as well as the participation of such users 110 in responding tosuch queries as a result of participation in the correspondingcontest/survey. Some entries of the personal data set of some users 110may accordingly be empty or may include assumed or predicted data, asdesired, when utilized by the corresponding machine learning program.

As used in various examples hereinafter, the personal data set of atleast one of the users 110 may include at least one data entry relatedto a past purchase or an ongoing use (active status) of a product and/orservice by the corresponding user 110 as offered by the enterprisesystem 200, or to the lack of the purchase or use of such a productand/or service by the corresponding user 110. The personal data set ofat least one of the users 110 may also include at least one data entryrelated to the frequency of use or manner of use of a certain productand/or service provided by the enterprise system 200 for use by thecorresponding user 110. The product and/or service for which thefrequency or classification of use is collected may correspond to thepreviously mentioned product and/or service for which the data regardingthe past purchase or ongoing use is collected. For example, if thecorresponding user 110 has an established credit card account with theenterprise system 200, the personal data set of the corresponding user110 may include a data entry relating to the active status of the creditcard account (thereby indicating the past purchase or ongoing usethereof), a data entry relating to the frequency of use of the creditcard account, and at least one data entry relating to the types oramounts of the transactions carried out with the credit card account.The personal data set of the corresponding user 110 may also includedata entries relating to the lack of purchase and/or use of otherproducts and/or services also provided by the enterprise system 200,such as alternative financial products and/or services that the user 110is not currently utilizing. The personal data set of the correspondinguser 110 may also, where applicable, include data entries relating tothe purchase and/or use of products and/or services provided by athird-party entity, such as entities representative of competitors tothe enterprise system 200. For example, the personal data set mayinclude data entries that indicate the presence of other credit cardaccounts associated with the corresponding user 110 with respect tothird-party financial institutions and any available data regarding thefrequency or manner of use of such third-party credit card accounts. Asevidenced by the examples set forth hereinafter, it should beappreciated that the personal data set associated with each of the users110 may include any combination of the data described as being availableto the computing system 206 herein while remaining within the scope ofthe present invention.

The present invention relates to various different machine learningprocesses carried out by the computing system 206 of the enterprisesystem 200 and suitable for predicting the behavior of a specific user110 based on the personal data set thereof at the time of the desiredprediction. The machine learning program may utilize any of theprocesses described herein, alone or in combination, while remainingwithin the scope of the present invention. The described processes aredrawn towards determining a propensity or probability for the specificuser 110 to purchase and/or otherwise interact with or utilize aspecific product and/or service offered by the enterprise system 200based on the predictive capabilities of a corresponding predictive modelgenerated by the machine learning program, or, alternatively, to predictthe response of a user 110 to a specific query in determining anadditional task to take with respect to the corresponding user 110.

A training data set utilized in performing the training of theassociated predictive model of the machine learning program may compriseany subset of the described types of personal data described herein withrespect to a plurality of the users 110, so long as the personal dataset of at least some of the users 110 forming the training data setinclude response data therein indicative of those users 110 havingresponded to a query as described herein. The training data set mayinclude the use of all entries of the personal data set associated witheach of the users 110 or may include the use of only specific dataentries associated with each of the users 110, such as only certaindemographic data, certain response data, or certain data relating to thepurchase and/or use of certain products and/or services offered by theenterprise system 200. The training data set may be limited to thepersonal data sets of only those users 110 having a specificclassification based on an analysis of the personal data set of each ofthe users 110 for which the computing system 206 has the necessary data.For example, the training data set may be limited to only those users110 having an active account with the enterprise system 200, only thoseusers 110 fitting into a specific demographic classification such asexceeding a certain age, only those users 110 that have alreadypurchased or are currently utilizing a selected product and/or serviceoffered by the enterprise system 200, only those users 110 that have notalready purchased or utilized a selected product and/or service offeredby the enterprise system 200, or only those users 110 having respondedto a corresponding query. The training data set may also include each ofthe data entries described as relating to a behavior such as thepurchase and/or ongoing use of a particular product and/or service beinglimited to those activities having occurred within a given time frame,such as those data entries representative of activity having occurredwithin the past month, the past 6 months, the past year, or anyalternative time frame.

According to some embodiments of the present invention, the machinelearning program utilizes unsupervised learning for determiningrelationships between the different data entries utilized in thetraining data set comprised of the personal data of the plurality of theusers 110. The unsupervised learning includes the training data formedby the personal data of the plurality of the users 110 being unlabeledwith respect to all entries. As such, none of the different possibledata entries is representative of a form of known output during theprocess of training the machine learning program. Each of the differentdata entries regarding a specific user 110, whether personal dataentries relating to demographic classifications, responses to queries,or past interactions or behaviors of the specific user 110 and/orenterprise system 200, may therefore form an independent unlabeled inputfor performing the unsupervised learning of the machine learningprogram.

As used hereinafter, the personal data set comprising the personal dataof a corresponding one of the users 110 that is utilized in training themachine learning program or performing a prediction via the predictivemodel generated by the machine learning program may alternatively bereferred to as the personal data profile of the corresponding user 110at the time at which such personal data set is utilized by the machinelearning program. For example, one specific user 110 may include apersonal data profile including a combination of demographic dataregarding the specific user 110 (age, income level, marital status,etc.), data regarding recorded interactions the specific user 110 hasengaged in with the enterprise system 200 (account transaction history,application browsing history, etc.), including the purchase and/or useof certain products and/or services, and data relating to the responsesto the queries. The personal data profile of a specific user 110 isaccordingly different each time the personal data set regarding the user110 as utilized by the machine learning program in making a predictionchanges, such as when certain entries indicate a change in value or achange in state or condition with respect to at least one entry of thepersonal data set of the user 110.

The machine learning program may be configured to perform clusteranalysis wherein the training data constituting the different entries ofthe personal data is grouped into subsets (clusters) wherein eachcluster is determined by the similarity of the data contained within thecluster with respect to a plurality of the users 110, or thedissimilarity with respect to data not within the cluster with respectto the plurality of the users 110, depending on the methodologyutilized. That is, each cluster includes a plurality of the users 110identified as forming the cluster having met a threshold degree ofsimilarity among the data corresponding to the plurality of the users110 according to a predefined similarity criteria. This clusteringallows for users 110 having a similarity of personal data profile, suchas a certain set of demographic traits, behavioral traits, and responsebased traits, to be grouped together in such clusters.

For example, a cluster of a plurality of the users 110 may include eachof the users 110 having a first demographic trait indicated by a firstdata entry, a second demographic trait indicated by a second data entry,a first product trait relating to the use of a first product indicatedby a third data entry, a second product trait relating to the use of asecond product indicated by a fourth data entry, and a first responsetrait relating to a specific response to a query. Such a cluster may beindicative of other users 110 that also share the first demographictrait, the second demographic trait, the first product trait, and thefirst response trait having an increased propensity to also share thesecond product trait. Alternatively, such a cluster may be indicative ofother users 110 that also share the first demographic trait, the seconddemographic trait, the first product trait, and the second product traithaving an increased propensity to also share the first response trait,thereby indicating that the other users are likely to respond to thequery in the same manner. Such associations may be utilized to determinethe probability of a specific user 110 being likely to take an actionresulting in the personal data set of the user 110 changing in a mannerindicative of a change in a specific data entry of the personal dataset. The unsupervised learning process accordingly allows for causalityto be implied between a particular personal data entry, such as an entryrelating to the purchase or use of a specific product and/or service,and any other subset of the personal data set, such as any combinationof demographic or behavioral data relating to other products and/orservices, by discovering a correlation between such common occurrencesof these data within the training data. Similarly, the unsupervisedlearning process may allow for causality to be implied between aparticular response to a query and any other subset of the personal dataset, such as any combination of demographic data, behavioral data, orresponse data.

The machine learning program may be configured to determine aprobability that a certain personal data profile of a specific user 110will correspond to a certain data entry related to the purchase and/oruse of a specific product and/or service offered by the enterprisesystem 20. Alternatively, the machine learning program may be configuredto determine a probability that a certain personal data profile of aspecific user 110 will correspond to a certain data entry relating to aspecific response to a specific query. The predictive model generated bythe machine learning program may be configured to assign a value to thelikelihood that any specific data entry may be expected to change orhave a certain value or state within the personal data set of thespecific user 110, where such a change in value/state or absolutevalue/state may be indicative of a new purchase or change in use of thecorresponding product and/or service by the specific user 110, or may beindicative of a specific response to a query posed to the user 110.Although described as a probability in each case, it should beappreciated that the machine learning program may alternatively assignbinary outcomes to any specific data entry, such as determining whethera purchase or specific response is not going to occur (0) or going tooccur (1) based on the predictive model, as desired.

With reference to a previously provided example, it may be discoveredvia application of the predictive model that a specific user 110 havingthe first demographic trait, the second demographic trait, the firstresponse trait, and the first product trait, but not the second producttrait, may have a relatively high probability of eventually changing thesecond product trait to match that of the users 110 forming the mostsimilar cluster to the specific user 110, such as eventually purchasingor using the second product, based on the manner in which similar users110 assigned to that cluster had previously purchased or used the secondproduct according to the data relating to the second product trait ofthese similar users 110. Similarly, it may be discovered via applicationof the predictive model that a specific user 110 having the firstdemographic trait, the second demographic trait, the first producttrait, and the second product trait, but who has not yet responded to aspecific query, may have a relatively high probability of choosing aresponse to the specific query that matches those of the users 110forming the most similar cluster to the specific user 110, based on themanner in which similar users 110 assigned to that cluster hadpreviously answered this same query.

The unsupervised learning process may be utilized exclusively to formthe predictive model of the machine learning program wherein predictionsmay be made regarding the likelihood of any specific entry having aspecific value/state or a specific change in the value/state of an entryoccurring with respect to any of a number of different characteristicsassociated with the user 110 for which the predictive model is beingapplied. The personal data profile of the user 110 may be utilized todetermine the likelihood of any number of different entries having aspecific value/state or change in value/state indicative of a certainrelationship being present between the user 110 and the enterprisesystem 200, including the determination of the probability of multipledifferent data entries associated with multiple different productsand/or services having a certain value/state and/or changing invalue/state in the future, or the likelihood of a user 110 responding toany specific query in a specific manner. Such predictive modeling mayaccordingly allow for the personal data profile of any specific user 110to be utilized in determining the likelihood of the specific user 110 inmaking a purchase or agreeing to use any number of the specific productsand/or services as supplied by the enterprise system 200, or indetermining the likelihood that the user 110 will provide a specificresponse to a specific query.

In other embodiments, the unsupervised learning process may be utilizedto discover those variables among the personal data entries used in thetraining data set that show correlation or causality in makingpredictions via one or more other predictive models associated with themachine learning program. These other predictive models may utilizesupervised training wherein only those data entries shown to be ofpredictive value in making an association between the different users110 during the unsupervised training are utilized, as desired.

The present invention may also utilize a semi-supervised learningprocess wherein the personal data set associated with each of the users110 is monitored following a prediction with respect to one of thecorresponding data entries such that certain data entries may besubsequently classified as labeled data during further training of themachine learning program. For example, the machine learning program maybe drawn towards determining the likelihood of a plurality of users 110choosing to purchase first product within the next six months. Sixmonths from the prediction of the purchase, each of the users 110 forwhich a prediction was made may have their instantaneous personal dataset utilized as a part of the training data used in further training ofthe predictive model, with those entries relating to the actual purchaseor lack of purchase of the first product being considered labeled data.The use of such labeled data allows for the data related to the firstproduct to be considered a form of output data that can further informthe predictive model by evaluating the precision or accuracy of thepredictions made by the previous iteration of the predictive model. Asanother example, the predictive model may predict the response of aspecific user 110 to a specific query, and may then compare the actualresponse of the user 110 to the predicted response upon the user 110later responding to the query. The response data relating to such aresponse would once again be considered the labeled data entry utilizedin the training data set of such a training process.

The machine learning program may alternatively, or additionally, utilizesupervised learning according to any of the supervised learningprocesses or tools disclosed herein. The supervised learning process mayinclude the data entry or entries relating to a desired product and/orservice being labeled as an output while the data entries relating tothe remaining personal data of each of the users 110 forming thetraining data (including, where applicable, the response data set of thecorresponding user 110) are labeled as inputs into the correspondingmodel. As such, the supervised learning process may include the outputof the predictive model corresponding to the probability or likelihoodof a specific event occurring, such as the purchase or use of a specificproduct and/or service by the corresponding user 110, whereas the inputinto the predictive model relates to the remaining personal data of thecorresponding user 110 for which the prediction is being made. Asanother example, the supervised learning process may include the outputof the predictive model corresponding to the probability or likelihoodof a specific response occurring with respect to a specific query posedto a specific user 110, whereas the input into the predictive modelrelates to the remaining personal data of the corresponding user 110 forwhich the prediction is being made. The supervised learning process mayaccordingly result in a predictive model that similarly providesinformation relating to a probability or likelihood that a specificevent will occur (or is already occurring) as indicated by at least oneentry of the personal data set of the user 110 for which the predictionis being made, such as the eventual purchase or use of a product and/orservice or the eventual selection of a specific response to a specificquery.

Regardless of the methodology utilized, the training process may includethe training data utilized in forming the predictive model divided intoinitial training data and validation training data. The initial trainingdata may be utilized to create an initial predictive model configured tomake a prediction with respect to at least one of the data entries ofthe user 110 associated with the user 110 purchasing or using a specificproduct and/or service or giving a specific response to a specificquery. The validation training data may then be utilized to makepredictions regarding each of the users 110 comprising the validationtraining data that can be evaluated relative to the already known dataentries relating to such predictions. For example, the data entries of auser 110 not relating to the desired product and/or service may beutilized to make a prediction regarding the purchase or use of thedesired product and/or service as indicated by one of the data entries,wherein the actual value of this data entry is already known from theentirety of the validation training data. The predicted outcome may thenbe compared to the actual outcome, as indicated in the known validationtraining data, and the accuracy and precision of the predictive modelmay then be evaluated based on the training that occurred with respectto the initial training data. Any difference between the predictedoutcome and the actual outcome may also be utilized as an error signalfor correcting the predictive model according to the correspondingmethodology thereof during the validation process.

Once the corresponding predictive model is shown to make predictions ordeterminations having a desired degree of accuracy or precision inaccordance with the desires of the enterprise system 200, the predictivemodel may be subjected to further training by evaluating the predictionsmade with respect to at least some of the users 110 at a first instanceto the actual data entries of those same users 110 at a second instance.For example, the predictive model may be configured to predict thelikelihood that a specific user 110 having a corresponding personal dataprofile will purchase a first product within the next six months,wherein such a prediction is made at a first instance. The machinelearning program may be further trained by evaluating the personal dataprofile of that same user 110 at a second instance, which is six monthsafter the first instance, to determine if the data entry indicative ofthe purchase of the first product has changed at the second instance inaccordance with the prediction made at the first instance. As anotherexample, the predictive model may be configured to predict thelikelihood that a specific user 110 having a corresponding personal dataprofile will give a specific response to a specific query when posed tothe user 110. The machine learning program may be further trained byevaluating the personal data profile of that same user 110 uponresponding to the query to determine if the data entry indicative of thechosen response occurred in accordance with the prediction of thepredictive model. Such data utilized in confirming the predictions ofthe predictive model may be referred to as the testing training datahereinafter. Such testing training data may accordingly be utilized tofurther refine the predictions made by the predictive model byevaluating the real world outcomes of the predictions made by thepredictive model.

FIG. 7 illustrates a method 1000 of implementing the machine learningprogram for predicting the likelihood of an event occurring with respectto a user 110 of the enterprise system 200 based on the personal dataprofile of the user 110 according to the present invention. The methodincludes an initial step 1001 of querying a plurality of the users 110for attaining one or more responses forming response data entriesutilized in the training data set. As mentioned above, the querying maybe conducted directly by the enterprise system 200 or by a third partyexternal source 202, 204, and may be initiated at the request of theuser 110 via participation in a corresponding contest, the enterprisesystem 200 via a corresponding communication or request, or the thirdparty external source 202, 204.

In some embodiments, the user 110 utilizes the web or softwareapplication 132 associated with the enterprise system 200 to conduct theprocess of responding to a specific query. In some circumstances, thecorresponding contest, giveaway, or sweepstakes may be made known toeach of the users 110 via an appropriate communication or advertisement,which may be made available via email, push notification, or display viaan interface of the application 132, as non-limiting examples. Upondetermining to participate in such a contest, the user 110 may bequeried during a process of enrolling in the contest, and such querymake take any of the forms described herein and may be directed towardsany of the different subject matters described herein. The querying mayinclude multiple queries being asked substantially contemporaneously,wherein each response is stored as an individual entry of the responsedata set corresponding to that user 110. The completion of one or moreof the queries may be made a term for eligibility in the underlyingcontest to ensure participation in responding to such queries whenposed. For example, providing responses to the queries may be necessaryto continue on during the process of enrolling in the contest, such asnot progressing to the next step of the enrollment process unless suchqueries are addressed appropriately in accordance with the terms of thecontest.

In some circumstances, the contest, giveaway, or sweepstakes may includemultiple different stages or periods of engagement or interaction withthe user 110, such as engaging the user 110 throughout a sports seasonor sports tournament where the outcome of individual games (or the like)are correlated to the success of the user 110, or where the users 110are competing against each other in some respect. Such contests mayrequire periodic or continued interaction from the user 110 asconditions change, such as picking new players or teams to utilize fromgame to game or week to week, depending on the format of the contest.Such contests may also include features wherein the user 110 may desireto login to the account of the user 110 via the web or softwareapplication 132 to track the progress of the user 110, such as thecurrent standings of the user 110 relative to other users 110. Each ofthe these continued engagements or interactions with the user 110 may beutilized as opportunities for the computing system 206 to further querythe user 110, hence some contests, giveaways, or sweepstakes may beassociated with multiple different instances of the queries being posedto a specific user 110, and hence multiple different queries or sets ofqueries.

In some other embodiments, the queries may not be linked directly to acontest, giveaway, drawing, or sweepstakes, and may instead be utilizedwith respect to alternative interactions occurring between the computingsystem 206 and the user device 104, 106 of the corresponding user 110.For example, such queries may be posed to the user 110 when navigatingthe web or software application 132 associated with the enterprisesystem 200, such as when certain resources are accessed, or when theuser 110 first logs into the corresponding account. In somecircumstances, such queries may be related to the topic or contentaccessed by the user 110, such as posing a query regarding theimpressions of the user 110 regarding certain financial instruments whenthe user 110 has accessed educational materials regarding such financialinstruments while navigating the interface of the web or softwareapplication 132. Such queries may form a portion of a survey provided bythe enterprise system 200 for completion by the user 110, which mayinclude a series of queries of related or unrelated content. The use ofa survey may include a prompt for completion of the survey to beautomatically and proactively displayed to the user 110, such as via aninterface of one of the user devices 104, 106 during navigation of theweb or software application 132, in order to increase participation insaid survey.

The method includes a step 1002 of collecting the personal data set withrespect to each of the users 110 as required for performing the trainingof the machine learning program as described hereinabove, oralternatively as required for inputting data into the predictive modelfor making a prediction via the predictive model. As describedhereinabove, such personal data may originate from any of the describedsources 110, 200, 202, 204 and may be communicated to the computingsystem 206 of the enterprise system 200 using any of the methods orcommunication channels described hereinabove. Certain proprietary dataare also collected directly by the enterprise system 200 as a result ofthe monitoring of the interactions of the enterprise system 200 and theuser 110 as described hereinabove.

A step 1003 includes training the machine learning program utilizing theapplicable training data to generate a predictive model having thecapabilities described herein. The predictive model may be acquiredutilizing any of the machine learning processes described herein withoutnecessarily departing from the scope of the present invention.

A step 1004 includes predicting the desired data entries of the personaldata set with respect to an individual user 110 using the predictivemodel of the machine learning program as based on the personal dataprofile of the user 110 at the time of the prediction. The predictingstep may include the machine learning program correlating the personaldata profile of the individual user 110 to the propensity for the user110 to undergo a change in the personal data profile thereof as causedby a change in at least one data entry thereof, such as the changing ofa data entry corresponding to the change in purchase status or thechange in use of a specific product and/or service offered by theenterprise system 200, or a change indicating that a specific query hasbeen responded to in a specific manner. The predicting step results inthe formation of output data which may be stored to the storage device224 of the computing system 206 as a form of the data 234. The outputdata generated by the predictive modeling of the machine learningprogram is referred to hereinafter as the prediction data of the machinelearning program, and may relate to the probability or likelihood of aspecific event occurring with respect to one or more of the relevantdata entries.

A step 1005 includes the computing system 206 of the enterprise system200 optionally performing a task in reaction to the generation of theprediction data with respect to the user 110. Such tasks may relate to acommunication being sent to the corresponding user 110 or a change inthe behavior of the computing system 206 to reflect the contents of theprediction data. These tasks are elaborated on in greater detailhereinafter.

A variety of different triggering conditions may be utilized by thecomputing system 206 in determining when the machine learning programshould execute the predictive aspects thereof to make a determination ofthe prediction data with regards to a specific user 110. In someembodiments, the prediction data may be determined with respect to aspecific user 110 when such an assessment is requested by an agent 210of the enterprise system 200 for the purpose of evaluating the bestactions to take with respect to the specific user 110. Such a requestmay be made of any number of the users 110, and may correspond to thepreparation of marketing materials corresponding to a product and/orservice offered by the enterprise system 200.

In other embodiments, the prediction data may be determined at fixedintervals, or otherwise on a fixed schedule. For example, the predictiondata may be determined with respect to each participating user 110 atregular intervals, such as daily, weekly, monthly, or quarterly, or maybe preprogrammed to occur on specific dates as requested by the agent210, as non-limiting examples.

In other embodiments, the prediction data may be determined when thepersonal data set of the specific user 110, as available for use intraining the machine learning program and/or executing any predictivecapabilities thereof, indicates that a triggering condition has occurredthat may be indicative of the need for an assessment of the user 110,such as the occurrence of an event shown to have a strong correlation toa change in an assessment of the user 110 regarding the predictionsrelating to the user 110. As non-limiting examples, the personal dataentries of the user 110 reflecting that the user 110 has reached acertain age, had a change in marital status, reached a certain accountbalance or status, or had a change in home ownership status may promptthe determination of the prediction data when such a change isdemonstrated to correlate to a change in the predictive assessment ofthe user 110, such as a change in the propensity for the user 110 topurchase or use a specific product and/or service. As another example,the triggering condition may relate to a data entry corresponding to therecent purchase or agreement to use a specific first product and/orservice as offered by the enterprise system 200, wherein it is knownthat the eventual purchase of a second product and/or service as offeredby the enterprise system 200 is correlated to the purchase of the firstproduct. As yet another example, the triggering condition may relate tothe initial entry of the response data set with respect to a specificuser 110 upon that user 110 responding to a corresponding query. Forexample, if a query directly requests a response indicating whether theuser 110 has interest in purchasing a product and/or service, a responseindicating such interest may trigger the making of the predictionregarding the likelihood of purchase of the corresponding product and/orservice.

Personal data specific to and accessible exclusively by the enterprisesystem 200 may be utilized in determining such a triggering condition.Such personal data may be acquired as a result of the relationshippresent between the enterprise system 200 and the user 110. For example,if the enterprise system 200 is a financial institution having access toaccount records, the triggering condition may relate to a certainbalance being reached within one of the accounts of the user 110, or toa failure of the user 110 to make a scheduled payment on a debt managedby the enterprise system 200, or the like. Such personal data mayaccordingly refer specifically to interactions between the user 110 andthe enterprise system 200 as a part of the relationship present betweenthe user 110 and the enterprise system 200, including data regardingpast transactions of the user 110 as initiated by the enterprise system200 or transactions occurring directly between the user 110 and theenterprise system 200. For example, the enterprise system 200 mayutilize data regarding purchases of the user 110 made with entitiesother than the enterprise system 200 (where such data is available, suchas where a financial instrument such as a credit card or debit cardassociated with the enterprise system 200 is used in making thesepurchases) or data regarding transactions including payments,agreements, or other contractual obligations made directly between theuser 110 and the enterprise system 200 with regards to a product and/orservice offered by the enterprise system 200.

Such data may also include data collected by the enterprise system 200from a third party source where the user 110 has provided expressconsent for such data to be shared with or otherwise accessible to theenterprise system 200, such as data regarding transactions occurringbetween the user 110 and entities external to the enterprise system 200that are not otherwise monitored directly by the enterprise system 200.For example, the enterprise system 200 may have access to data regardingtransactions occurring with respect to a credit card or debit card ofthe user 110 associated with and/or managed by a financial institutionother than the enterprise system 200, hence such data must becommunicated to the enterprise system 200 for access thereto indetermining such triggering events.

The enterprise system 200 may also utilize personal data collected withrespect to the user 110 regarding the interactions of the user 110 withthe enterprise system 200 via the corresponding web browser applicationor software application 132 associated with the enterprise system 200.For example, the navigating of the application 132 may include the user110 reviewing information relating to certain products and/or servicesoffered by the enterprise system 200, or making a selection thatadditional information is requested with respect to a topic related toone of the queries of the survey corresponding to the prediction data.Similar data may be collected regarding alternative interactions, suchas whether or not the specific user 110 views or responds to emailmessages, text messages, or the like, as applicable. The determinationof the prediction data based on such interactions may aid in proactivelyassessing the user 110 and offering intervention by the enterprisesystem 200, such as allowing the enterprise system 200 to offer certainproducts and/or services when it has been determined that such productsand/or services have been reviewed by the user 110 in conjunction withthe data profile of the user 110, thereby indicating a need of the user110 to attain such a product and/or service.

The triggering conditions indicated above may also be complex in natureand may include reference to multiple different variables of thepersonal data of the user 110 or multiple conditional relationshipstherebetween. As one example, upon determining that the age of the user110 has surpassed a certain threshold, an additional variable of thepersonal data of the user 110, such as the balance of a savings accountof the user 110 accessible to the enterprise system 200, may be utilizedin determining whether the prediction data must be determined andfurther utilized. Specifically, with respect to the given example, thetriggering of the determination of the prediction data may include thedetermination being made only if the age of the user 110 meets orexceeds the established threshold and the data regarding the accountbalance also meets or exceeds the established threshold. It should alsobe appreciated that the prediction data may be generated based on anycombination of any of the above described conditions or events, asdesired.

In some embodiments, the computing system 206 of the enterprise system200 may continuously determine the prediction data with respect to eachparticipating user 110 whenever additional personal data is acquired bythe computing system 206 regarding the user 110 that has been utilizedin training the machine learning program, whether derived from aninteraction between the user 110 and the enterprise system 200 oracquired by the enterprise system 200 from a third party source 202,204. This allows the prediction data corresponding to any one user 110to always be as up to date as possible, thereby providing a semi-realtime assessment of the user 110 via the prediction data, whetherpredicting possible purchases or possible query responses.

With renewed reference to step 1005 of FIG. 7 , the enterprise system200 may utilize the prediction data determined with respect to each ofthe corresponding users 110 for performing a variety of different tasksonce such prediction data has been determined. In some circumstances,the prediction data is utilized by the enterprise system 200 to makedeterminations regarding further interactions with the user 110 orchanges in behavior of the enterprise system 200. Such determinationsmay include whether to intervene by offering certain products and/orservices to the user 110 based on the likelihood of the user 110 toengage with the products and/or services as indicated by the predictiondata.

The computing system 206 may determine to utilize the prediction datafor performing a specific task at step 1005 depending on a variety ofdifferent factors, including the use of several triggering conditions insimilar fashion to the description of when a determination of theprediction data is to be determined with respect to a user 110 asdescribed hereinabove with respect to step 1004. As mentioned before,each event predicted by the predictive model may be associated with aprobability within the prediction data, and such probabilities ofcertain events occurring may represent the triggering condition for thecomputing system 206 to take further action with respect to a certainproduct and/or service. For example, each different product and/orservice associated with the predictive model may have a unique thresholdvalue that must be met or exceeded for the computing system 206 to takeaction as described hereinafter. If multiple products are beingevaluated by the predictive model, only those products indicated by thepredictive model has having a certain likelihood of engagement (such aspurchase thereof) may be associated with a communication from thecomputing system 206. The computing system 206 may also be configured toonly send communications with respect to those products and/or servicesranked as being the most likely to be positively engaged with by theuser 110, so as to avoid overwhelming the user 110 with excessive offersor promotions.

The communication to the corresponding user 110 from the enterprisesystem 200 may occur using any known communication method. For example,an email, text message, push notification, or the like may be generatedby the computing system 206 for communication to the corresponding user110. Such a communication may be communicated from the computing system206 to the user device 104, 106 of the user 110 using any of the methodsdescribed hereinabove in describing the communication capabilities ofthe devices 104, 106 and systems 200, 206 within FIG. 1 . The user 110may then review such a communication via interaction with thecorresponding user device 104, 106, which provides a perceptibleexpression of the content of the communication. Such a perceptibleexpression of the content of the communication may include theinformation being communicated being visually perceptible, such as inthe form of readable text able to be displayed on the user device 104,106, or audibly perceptible, such as in the form of an audio file ableto be played by the user device 104, 106. The display 140 of the userdevice 106 or the speaker 144 of the user device 106 may be utilized inperceiving the content of the communication.

One potential action may include the enterprise system 200 offeringproducts and/or services to the user 110 in reaction to an analysis ofthe prediction data specific to the user 110. The described offer ofproducts and/or services from the enterprise system 200 may take manydifferent forms. In some circumstances, the offer may constitute anoffer of educational materials or advertising materials regarding aspecific product and/or service for which the user 110 is determined tohave a propensity to purchase or use, as revealed by the prediction dataassociated with a corresponding user 110. Such an interaction may, inthe case of educational materials, include a communication from thecomputing system 206 of the enterprise system 200 to the user device104, 106 of the user 110, such as by an email, text message, or pushnotification, as non-limiting examples. Such educational materials mayadditionally or alternatively be made available for access via theaccount of the corresponding user 110 when accessing the web browserapplication or software application 132 corresponding to the enterprisesystem 200.

In other circumstances, the communication from the computing system 206to the user device 104, 106 may include a direct offer for the productand/or service to be purchased by the user 110 or otherwise provided bythe enterprise system 200 to the user 110 by express agreement. Forexample, the communication to the user 110 may include an offer topurchase an investment related product and/or service from theenterprise system 200, or may include an offer for the enterprise system200 to offer periodic advise to the user 110 regarding topics such aslong-term planning as provided by an agent 210. Such an offer mayinclude the communication from the computing system 206 to the userdevice 104, 106 including information for redirecting the user device104, 106, such as may occur via use of the appropriate web browserapplication or software application 132, to an appropriate interface forcompleting the purchase of the product and/or service, as may beaccessible via the application 132.

In some circumstances, the offer of the products and/or services to theuser 110 may also include a reference to the personal data of thecorresponding user 110 for prepopulating data related to the purchase ofthe product and/or service. For example, if the process of completingthe purchase of the product and/or service includes the user 110entering information into an interface provided by the webpage orapplication 132, the personal data of the user 110 as known by thecomputing system 206 may be utilized to automatically prepopulate anyfields of the purchase process to which the personal data corresponds.As another example, the purchase of some products and/or services mayrequire documents to be populated with such personal data, hence theenterprise system 200 may be configured to automatically prepopulatesuch documents prior to communicating such documents to the user 110.Such documents may comprise transferable files of any desired typecompatible with each of the associated devices or computing systems 104,106, 206.

The computing system 206 may accordingly utilize the prediction data, asdetermined by the machine learning algorithm, in providing an output inthe form of a communication or message relating to a product and/orservice that may be likely to be of interest to the user 110 or apreference of the user 110, wherein such communication or message mayconstitute an offer for purchase of the product and/or service. Such acommunication or message may further include output in the form of aprepopulated document/file or a prepopulated payment interface relatingto the corresponding purchase/agreement in an attempt to provide saidproduct and/or services to the user 110. Such a prepopulated document orinterface may be accessible via use of the user device 104, 106following a transfer of the file/data to the user device 104, 106 fromthe computing system 206.

The computing system 206 may alternatively alter the account settings ofthe user 110 in a manner altering a manner in which the computing system206 interacts with the user 110 via the corresponding user device 104,106 in response to the generation of the prediction data regarding theuser 110. For example, such account setting changes may include changingthe settings relating to the frequency of communications sent from thecomputing system 206 to the user 110 for access via the user device 104,106, under what conditions to communicate with the user 110, the contentof such communications, the types or forms of such communications (suchas paperless communication), the manner in which the interface of theweb browser application or software application 132 displays informationto the user 110, or the information or resources accessible to the user110 via navigation of the web browser application or softwareapplication 132, as non-limiting examples. The changing of the accountsettings may refer to the computing system 206 altering the accountrelated data stored as a form of the data 234 associated with thestorage device 224, which in turn results in a reconfiguring of theoperation of the computing system 206 with regards to how the computingsystem 206 subsequently interacts with the user device 104, 106 withrespect to at least one variable.

As explained throughout, the predictive model generated by the machinelearning program may be configured to make a prediction regardingsubstantially any data entry relating to the personal data set of thecorresponding user 110, whether related to the query responses or theremaining personal data of the corresponding user 110. The predictivemodel may make predictions about the traits or behaviors of the user 110following (and potentially partially based upon) the response to a queryor may make predictions regarding a future response to a user 110 havingnot yet responded to a query. Examples of the execution of method 1000with respect to various different applications of the predictive modelare elaborated on hereinafter.

In some embodiments, the predictive model may be utilized to determinethe propensity of a specific user 110 to engage with a specific productand/or service, such as making a purchase of the specific product and/orservice. The specific user 110 may or may not be a user 110 that hasresponded to one or more queries to provide additional personal datawith which to correlate the user 110 to other users 110 having alsoresponded to the query via use of the predictive model. The predictionregarding the probability or propensity for an engagement such as apurchase to happen may occur following the occurrence of any of thetriggering conditions listed herein, such as the responding of the user110 to a query related to the product and/or service in question. Theprediction may result in a further determination by the computing system206 on whether to take further action, such as comparing the determinedprobability of the action in question to a threshold value. One possiblefurther action includes the generation of a communication regarding theproduct and/or service that is sent by the computing system 206 to theuser device 104, 106 when such a threshold is met or exceeded.

The predictive model may be configured to make a prediction regardingthe preference of the user 110 regarding various different accountsettings or the like associated with the manner in which the user 110interacts with the computing system 206 or the enterprise system 200.For example, the personal data profile of the user 110 may indicate thatthe user 110 is likely to adopt or prefer a specific account settingrelating to the number, type, or form of communications occurringbetween the computing system 206 and the user device 104, 106. Inreaction to this prediction, the computing system 206 may requestconfirmation from the user 110 of such a change to the account setting,or the computing system 206 may automatically make the adjustment to theaccount setting in the absence of user 110 approval, where applicable.

In other embodiments, the predictive model is utilized to predict theresponse of the user 110 to a query where the predicted response isitself utilized in making a further determination regarding theactivities of the computing system 206. For example, as mentioned above,some queries may directly relate to the interest of the user 110 withrespect to one or more specific products and/or services, or maydirectly request a ranking or preference among such products and/orservices. In such instances, the selection of the user 110 of a certainproduct and/or service in responding to a query may itself be atriggering event for causing further action to be taken in accordancewith step 1005 of method 1000. In such a circumstance, the predictivemodel may be utilized to predict the response of the user 110 to such atriggering query, wherein such a prediction of the response may besubstituted for the actual response of the user 110 in making anassessment of the further action to be taken by the computing system206. As such, the use of the predictive model in predicting a specificresponse allows for the ability to target users 110 believed to haveinterest in a specific product and/or service absent these users 110having actually responded to the query in question, or havingparticipated in an accompanying contest. Specifically, the predictiondata relating to the probability of a certain event occurring, such as apurchase of a specific product and/or service, may be utilized forcomparison to a threshold value for determining if an action such as thegeneration of a communication or the change of the user account settingis required to address this prediction.

As another related example, the query response being predicted mayrelate to a preference of the user 110 regarding certain futureinteractions between the user 110 and the computing system 206 and/orenterprise system 200, such as would be associated with a specificaccount setting of the user 110. For example, the query may ask whatmethod of communication as utilized by the computing system 206 and/orthe enterprise system 200 is preferred by the user 110 under a certaincircumstance. The ability to predict this response allows for thecomputing system 206 to proactively request or automatically make such achange to the account settings of the user 110 regardless of the user110 having specifically engaged in the contest and corresponding queryin question.

The use of the machine learning program and resulting predictive modelimproves the efficiency of the operation of the computing system 206 invarious different respects. First, the disclosed method provides anability for the computing system 206 to eliminate unnecessarycalculations and communications relating to certain tasks performed bythe computing system 206 that have been found to not have a positiveimpact on securing the desired product and/or service from the user 110.This may be especially relevant where such marketing materials are to beproduced in hard copy form and mailed to the user 110, as extensivecosts can be avoided by targeting the correct users 110. This results inthe computing system 206 avoiding a waste of resources when performingcertain tasks, such as sending unnecessary communications of variousforms to users 110 that will never interact with or benefit from thesending of such communications. Second, the use of the machine learningprogram also allows for certain variables in the personal data setsutilized in the training process to be determined to be irrelevant tocertain relationships. The identification of these variables that do notpredict any specific result may be omitted from further analysis or mayno longer be monitored by the computing system 206 in forming thepersonal data sets. The predictive model accordingly provides a means toidentify those data that are not necessary to be tracked or collectedand further allows for the calculations occurring via the computingsystem 206 to be simplified by means of the elimination of additionalvariables. Third, the machine learning program provides the enterprisesystem 200 greater insight to determine other related actions to takethat may increase the probability of the completion of a sale or thelike with respect to a specific product and/or service, or in improvingthe preference of the user 110 with respect to a desired accountsetting. Each of the described advantages leads to a reduction innetwork traffic as experienced by the computing system 206 due to theability to target only those communications predicted to have apreselected probability of having a positive impact on the engagementwith the corresponding users.

Particular embodiments and features have been described with referenceto the drawings. It is to be understood that these descriptions are notlimited to any single embodiment or any particular set of features.Similar embodiments and features may arise or modifications andadditions may be made without departing from the scope of thesedescriptions and the spirit of the appended claims.

From the foregoing description, one ordinarily skilled in the art caneasily ascertain the essential characteristics of this invention and,without departing from the spirit and scope thereof, can make variouschanges and modifications to the invention to adapt it to various usagesand conditions.

What is claimed is:
 1. A system for training a machine learning modelfor interactions with a user device, the system comprising: a computerwith one or more processor and memory, wherein the computer executescomputer-readable instructions to guide the interactions with the userdevice; and a network connection operatively connecting the user deviceto the computer; wherein, upon execution of the computer-readableinstructions, the computer performs steps comprising: requesting aninitial response to one of a plurality of initial queries from aplurality of users, wherein the initial response is requested when eachof the respective users is enrolling or participating in a first stageof a multi-stage contest, wherein eligibility for a prize of themulti-stage contest requires each corresponding user providing theinitial response to the one of the plurality of the initial queries,wherein the plurality of initial queries includes a first initial queryand a second initial query different from the first initial query,wherein the plurality of users includes a plurality of first usersrequested to provide the initial response to the first initial query andnot the second initial query and a plurality of second users requestedto provide the initial response to the second initial query and not thefirst initial query; storing the initial response of each of theresponding users as initial response data, the initial response dataforming a subset of a personal data set of each of the responding users;generating an initial predictive model during initial training of amachine learning program utilizing at least one neural network, aninitial training data set utilized during the initial training of themachine learning program comprising the personal data set of each of theplurality of users following the storing of the initial response of eachof the responding users as initial response data; predicting, by theinitial predictive model, a predicted response of each of the firstusers to the second initial query; requesting a validation response to avalidation query from each of the first users, wherein the validationresponse is requested when each of the first users is enrolling orparticipating in a second stage of the multi-stage contest, whereineligibility for a prize of the multi-stage contest for each of the firstusers requires the corresponding first user providing the validationresponse to the validation query, wherein the validation query includescontent corresponding to that included in the second initial query suchthat the validation response of each of the first users can be comparedto the predicted response of each of the respective first users todetermine whether the initial predictive model correctly predicted thepredicted response of each of the first users to the second initialquery; storing the validation response of each of the responding firstusers as validation response data, the validation response data forminga subset of the personal data set of each of the responding first users;and generating a validation predictive model during validation trainingof the machine learning program, wherein a validation training data setutilized during the validation training of the machine learning programcomprises the personal data set of each of the plurality of usersfollowing the storing of the validation response of each of theresponding first users as validation response data, wherein a differencebetween the predicted response and the validation response with respectto each of the first users is utilized as an error signal for correctingthe initial predictive model during the generating of the validationpredictive model.
 2. (canceled)
 3. The system of claim 1, wherein theinitial response is related to a future use of a prize of themulti-stage contest by the corresponding user.
 4. The system of claim 3,wherein the initial response is related to the corresponding userpurchasing the a first product and/or service via use of the prize ofthe multi-stage contest.
 5. The system of claim 1, wherein the initialresponse includes information regarding a preferred interaction betweenthe computer and the corresponding user.
 6. The system of claim 5,wherein the preferred interaction refers to a form, frequency, orcontent of communications sent to the corresponding user by thecomputer.
 7. The system of claim 1, wherein the initial response isrequested when each of the plurality of users is navigating a softwareapplication executed on a corresponding user device.
 8. The system ofclaim 1, wherein the initial training of the machine learning programincludes unsupervised learning wherein each of the entries of theinitial training data set is unlabeled.
 9. The system of claim 1,wherein the machine learning program is configured to perform clusteranalysis with respect to the initial training data set during theinitial training of the initial predictive model.
 10. The system ofclaim 1, wherein the at least one neural network generates aself-organizing map.
 11. The system of claim 1, wherein the validationtraining of the machine learning program includes semi-supervisedlearning, wherein during the semi-supervised learning at least one ofthe entries of the validation response data is labeled .
 12. The systemof claim 1, wherein the initial training of the machine learning programincludes supervised learning with each of the entries of the initialtraining data set being labeled.
 13. The system of claim 1, wherein,upon execution of the computer-readable instructions, the computerfurther performs steps comprising: predicting, by the validationpredictive model, a probability of a first one of the users associatedwith the user device interacting with a first product and/or service,the predicting of the probability including the validation predictivemodel correlating a personal data set of the first one of the users tothe personal data set of at least a second one of the users; andsending, via the network connection, a communication to the user deviceof the first one of the users when the predicted probability meets orexceeds a threshold value, the communication including content relatingto the first product and/or service.
 14. The system of claim 13, whereinthe communication includes one of educational materials related to thefirst product and/or service and/or an offer for sale of the firstproduct and/or service.
 15. The system of claim 13, wherein the sendingof the communication to the user device of the first one of the usersincludes sending a document having prepopulated fields based onreference to the personal data set of the first one of the users. 16.The system of claim 15, wherein the document relates to an offer forsale of the first product and/or service.
 17. The system of claim 13,wherein the personal data set of each of the users includes demographicdata.
 18. The system of claim 13, wherein the personal data set of eachof the users includes a transaction history of the corresponding user.19. A method using a machine learning program for guiding interactionswith a user device, the method comprising the steps of: requesting, by acomputer, an initial response to one of a plurality of initial queriesfrom a plurality of users, wherein the initial response is requestedwhen each of the respective users is enrolling or participating in afirst stage of a multi-stage contest, wherein eligibility for a prize ofthe multi-stage contest requires each corresponding user providing theinitial response to the one of the plurality of the initial queries,wherein the plurality of initial queries includes a first initial queryand a second initial query different from the first initial query,wherein the plurality of users includes a plurality of first usersrequested to provide the initial response to the first initial query andnot the second initial query and a plurality of second users requestedto provide the initial response to the second initial query and not thefirst initial query; storing the initial response of each of theresponding users as initial response data, the initial response dataforming a subset of a personal data set of each of the responding users;generating an initial predictive model during initial training of themachine learning program utilizing at least one neural network, aninitial training data set utilized during the initial training of themachine learning program comprising the personal data set of each of theplurality of users following the storing of the initial response of eachof the responding users as initial response data; predicting, by theinitial predictive model, a predicted response of each of the firstusers to the second initial query; requesting a validation response to avalidation query from each of the first users, wherein the validationresponse is requested when each of the first users is enrolling orparticipating in a second stage of the multi-stage contest, whereineligibility for a prize of the multi-stage contest for each of the firstusers requires the corresponding first user providing the validationresponse to the validation query, wherein the validation query includescontent corresponding to that included in the second initial query suchthat the validation response of each of the first users can be comparedto the predicted response of each of the respective first users todetermine whether the initial predictive model correctly predicted thepredicted response of each of the first users to the second initialquery; storing the validation response of each of the responding firstusers as validation response data, the validation response data forminga subset of the personal data set of each of the responding first users;and generating a validation predictive model during validation trainingof the machine learning program, wherein a validation training data setutilized during the validation training of the machine learning programcomprises the personal data set of each of the plurality of usersfollowing the storing of the validation response of each of theresponding first users as validation response data, wherein a differencebetween the predicted response and the validation response with respectto each of the first users is utilized as an error signal for correctingthe initial predictive model during the generating of the validationpredictive model .
 20. The method of claim 19, wherein the initialresponse is requested when each of the plurality of users is navigatinga software application executed on a corresponding user device, whereinthe software application is managed by the computer.